d1.3 agriculture pilot final report · o pilot b1.2: cereals and biomass and cotton crops 2 o pilot...
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This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee.
Project Acronym: DataBio
Grant Agreement number: 732064 (H2020-ICT-2016-1 – Innovation Action)
Project Full Title: Data-Driven Bioeconomy
Project Coordinator: INTRASOFT International
DELIVERABLE
D1.3 – Agriculture Pilot Final Report
Dissemination level PU -Public
Type of Document Report
Contractual date of delivery M36 – 31/12/2019
Deliverable Leader TRAGSA
Status - version, date Final – v1.2, 21/1/2020
WP / Task responsible WP1
Keywords: Agriculture, pilot, Big Data, modelling, stakeholders,
final results
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Executive Summary D1.1 “Agriculture Pilot Definition”, submitted on the initial stage of the agriculture pilots,
reported the definition of use cases and the description of their requirements, collected
through a collaborative effort involving Big Data Technology (BDT) experts, end users and
other relevant stakeholders. D1.2 “Agriculture Pilots intermediate report” presented the
agriculture pilot intermediate progress, being focused on DataBio Trail 1 results. This current
document, DataBio deliverable D1.3 “Agriculture Pilot Final Report”, refers to the entire
agriculture pilot report and final WP1 DataBio outcomes.
All the deliverables are in line with the objective of WP1 “Agriculture pilot” which is to
demonstrate how technologies dealing with Big Data will be implemented into pilots and
validated on real-world cases in order to fulfil the end user communities’ expectations.
D1.3 highlights the results from agriculture pilots mostly from the second period (2018-2019)
trials, i.e. Trial 2, as a consequence of experimentations ran in Trial 1.
A total of 13 pilots have been completed in DataBio project testing Big Data technologies in
key areas of interest including horticulture, arable farming, subsidies and insurance, with the
ultimate aim of addressing different challenges facing the EU’s agriculture ecosystems:
(A) Precision Horticulture including vine and olives:
• Group A1: Precision agriculture in olives, fruits, grapes and vegetables
o Pilot A1.1: Precision agriculture in olives, fruits, grapes
o Pilot A1.2: Precision agriculture in vegetable seed crops
o Pilot A1.3: Precision agriculture in vegetables -2 (Potatoes)
• Group A2: Big Data management in greenhouse eco-systems
o Pilot A2.1: Big Data management in greenhouse eco-systems
(B) Arable Precision Farming:
• Group B1: Cereals and biomass crops
o Pilot B1.1: Cereals and biomass crops
o Pilot B1.2: Cereals and biomass and cotton crops 2
o Pilot B1.3: Cereals and biomass crops 3
o Pilot B1.4: Cereals and biomass crops 4
• Group B2: Machinery management
o Pilot B2.1: Machinery management
(C) Subsidies and insurance:
• Group C1: Insurance
o Pilot C1.1: Insurance (Greece)
o Pilot C1.2: Farm Weather Insurance Assessment
• Group C2: CAP support
o Pilot C2.1: CAP Support
o Pilot C2.2: CAP Support (Greece)
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The present document offers the final outcomes of the Tasks 1.2, 1.3 and 1.4 where Big Data
were exploited in the pilots together with IoT (Internet of Things) sensor data and EO (Earth
Observation) data; e.g. involving multispectral data and satellite imagery-derived markers as
NDVI (Normalized Difference Vegetation Index) indexes or biophysical parameters such as
fAPAR (fraction of Absorbed Photosynthetically Active Radiation) and several algorithms (as
machine learning techniques). DataBio platform technological components were deployed
through several applications including the development of irrigation needs algorithms, in
order to obtain full functionality in web applications based on high frequency, scalable
satellite image data at local and national level. Crop monitoring was carried out in order to
fine-tune the models to plant growth, development and performance, and health. The results
achieved in the second and final trial were satisfactory, and this document is a succinct
summary as measured against the defined objectives.
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Deliverable Leader: Jesús Estrada (TRAGSATEC)
Contributors:
Savvas Rogotis, Natassa Miliaraki, Kostas Mastrogiannis (NP)
Isabelle Picard (VITO)
Balestri Stefano (CAC)
Nicole Bartelds (NB Advies)
Ephrem Habyarimana (CREA)
Jesús Estrada (TRAGSATEC)
Karel Charvat Jr., Lukas Vojtěch (LESPRO)
Jaroslav Šmejkal (Zetor)
Raul Palma (PSNC)
Antonella Catucci, Laura De Vendictis, Alessia Tricomi (e-geos)
Maria Luisa Quarta (MEEO)
Maria Plakia, Dimitris Karamitros (EXUS)
Adrian Stoica, Olimpia Copacenaru (TerraS)
Reviewers:
Savvas Rogotis (NP)
Anagnostis Argiriou, Sofia Michailidou (CERTH)
Tomas Mildorf (UWB)
Approved by: Athanasios Poulakidas (INTRASOFT)
Document History
Version Date Contributor(s) Description
0.1 30/10/2019 Jesús Estrada,
Savvas Rogotis,
Karel Charvat Jr.
Table of Contents
0.2 29/11/2019 All contributors First draft
0.3 6/12/2019 E. Habyarimana Pilots A2.1 and B1.3
0.4 10/12/2019 Reviewers Editorial review
0.5 28/12/2019 Jesús Estrada Final draft
1.0 31/12/2019 A. Poulakidas Final version for submission
1.1 20/01/2020 Jesús Estrada Updated input from partners
1.2 21/01/2020 A. Poulakidas Final version for resubmission
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Table of Contents EXECUTIVE SUMMARY ....................................................................................................................................... 2
TABLE OF CONTENTS .......................................................................................................................................... 5
TABLE OF FIGURES ............................................................................................................................................. 9
LIST OF TABLES ................................................................................................................................................. 14
DEFINITIONS, ACRONYMS AND ABBREVIATIONS ............................................................................................ 15
1 INTRODUCTION ...................................................................................................................................... 17
PROJECT SUMMARY ................................................................................................................................. 17 DOCUMENT SCOPE .................................................................................................................................. 19 DOCUMENT STRUCTURE ........................................................................................................................... 19
2 AGRICULTURE PILOTS SUMMARY ........................................................................................................... 20
OVERVIEW ............................................................................................................................................. 20 INTRODUCTION OF PILOT CASES................................................................................................................... 21
3 PILOT 1 [A1.1] PRECISION AGRICULTURE IN OLIVES, FRUITS, GRAPES ................................................... 27
PILOT OVERVIEW ..................................................................................................................................... 27 SUMMARY OF PILOT BEFORE TRIAL 2 ............................................................................................................ 28 PREPARATION AND EXECUTION OF TRIAL 2 .................................................................................................... 28
Trial 2 timeline........................................................................................................................... 28 Preparation for Trial 2 ............................................................................................................... 28 Trial 2 execution ........................................................................................................................ 31 Trial 2 results ............................................................................................................................. 35
COMPONENTS, DATASETS AND PIPELINES ...................................................................................................... 36 DataBio component deployment status ..................................................................................... 36 Data Assets ............................................................................................................................... 37
EXPLOITATION AND EVALUATION OF PILOT RESULTS ......................................................................................... 38 Pilot exploitation based on results ............................................................................................. 38 KPIs ........................................................................................................................................... 39
4 PILOT 2 [A1.2] PRECISION AGRICULTURE IN VEGETABLE SEED CROPS ................................................... 43
PILOT OVERVIEW ..................................................................................................................................... 43 SUMMARY OF PILOT BEFORE TRIAL 2 ............................................................................................................ 43 PREPARATION AND EXECUTION OF TRIAL 2 .................................................................................................... 46
Trial 2 timeline........................................................................................................................... 46 Preparation and execution of Trial 2 .......................................................................................... 46 Trial 2 results ............................................................................................................................. 46
COMPONENTS, DATASETS AND PIPELINES ...................................................................................................... 54 DataBio component deployment status ..................................................................................... 54 4.4.3 Data Assets ....................................................................................................................... 55
EXPLOITATION AND EVALUATION OF PILOT RESULTS ......................................................................................... 55 Pilot exploitation based on results ............................................................................................. 55 KPIs ........................................................................................................................................... 55
5 PILOT 3 [A1.3] PRECISION AGRICULTURE IN VEGETABLES_2 (POTATOES) .............................................. 57
PILOT OVERVIEW ..................................................................................................................................... 57 SUMMARY OF PILOT BEFORE TRIAL 2 ............................................................................................................ 57 PREPARATION AND EXECUTION OF TRIAL 2 .................................................................................................... 59
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Trial 2 timeline........................................................................................................................... 59 Preparation for Trial 2 ............................................................................................................... 59 Trial 2 execution ........................................................................................................................ 62 Trial 2 results ............................................................................................................................. 68
COMPONENTS, DATASETS AND PIPELINES ...................................................................................................... 74 DataBio component deployment status ..................................................................................... 74 Data Assets ............................................................................................................................... 75
EXPLOITATION AND EVALUATION OF PILOT RESULTS ......................................................................................... 75 Pilot exploitation based on results ............................................................................................. 75 KPIs ........................................................................................................................................... 76
6 PILOT 4 [A2.1] BIG DATA MANAGEMENT IN GREENHOUSE ECO-SYSTEM .............................................. 81
PILOT OVERVIEW ..................................................................................................................................... 81 SUMMARY OF PILOT BEFORE TRIAL 2 ............................................................................................................ 82 PREPARATION AND EXECUTION OF TRIAL 2 .................................................................................................... 85
Trial 2 timeline........................................................................................................................... 85 Preparation for Trial 2 ............................................................................................................... 85 Trial 2 execution ........................................................................................................................ 85 Trial 2 results ............................................................................................................................. 90
COMPONENTS, DATASETS AND PIPELINES ...................................................................................................... 94 DataBio component deployment status ..................................................................................... 94 Data Assets ............................................................................................................................... 94
EXPLOITATION AND EVALUATION OF PILOT RESULTS ......................................................................................... 95 Pilot exploitation based on results ............................................................................................. 95 KPIs ........................................................................................................................................... 95
7 PILOT 5 [B1.1] CEREALS AND BIOMASS CROP ......................................................................................... 97
PILOT OVERVIEW ..................................................................................................................................... 97 SUMMARY OF PILOT BEFORE TRIAL 2 ............................................................................................................ 97 PREPARATION AND EXECUTION OF TRIAL 2 .................................................................................................... 98
Trial 2 timeline........................................................................................................................... 98 Preparation for Trial 2 ............................................................................................................... 99 Trial 2 execution ........................................................................................................................ 99 Trial 2 results ........................................................................................................................... 101
COMPONENTS, DATASETS AND PIPELINES .................................................................................................... 104 DataBio component deployment status ................................................................................... 109 Data Assets ............................................................................................................................. 110
EXPLOITATION AND EVALUATION OF PILOT RESULTS ....................................................................................... 110 Pilot exploitation based on results ........................................................................................... 112 KPIs ......................................................................................................................................... 113
8 PILOT 6 [B1.2] CEREALS, BIOMASS AND COTTON CROPS_2.................................................................. 114
PILOT OVERVIEW ................................................................................................................................... 114 SUMMARY OF PILOT BEFORE TRIAL 2 .......................................................................................................... 115 PREPARATION AND EXECUTION OF TRIAL 2 .................................................................................................. 115
Trial 2 timeline......................................................................................................................... 115 Preparation for Trial 2 ............................................................................................................. 115 Trial 2 execution ...................................................................................................................... 118 Trial 2 results ........................................................................................................................... 120
COMPONENTS, DATASETS AND PIPELINES .................................................................................................... 120 DataBio component deployment status ................................................................................... 120 Data Assets ............................................................................................................................. 121
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EXPLOITATION AND EVALUATION OF PILOT RESULTS ....................................................................................... 122 Pilot exploitation based on results ........................................................................................... 122 KPIs ......................................................................................................................................... 123
9 PILOT 7 [B1.3] CEREAL AND BIOMASS CROPS_3 ................................................................................... 124
PILOT OVERVIEW ................................................................................................................................... 124 SUMMARY OF PILOT BEFORE TRIAL 2 .......................................................................................................... 125 PREPARATION AND EXECUTION OF TRIAL 2 .................................................................................................. 127
Trial 2 timeline......................................................................................................................... 127 Preparation for Trial 2 ............................................................................................................. 127 Trial 2 execution ...................................................................................................................... 127 Trial 2 results ........................................................................................................................... 129
COMPONENTS, DATASETS AND PIPELINES .................................................................................................... 131 DataBio component deployment status ................................................................................... 131 Data Assets ............................................................................................................................. 131
EXPLOITATION AND EVALUATION OF PILOT RESULTS ....................................................................................... 132 KPIs ......................................................................................................................................... 132
10 PILOT 8 [B1.4] CEREALS AND BIOMASS CROPS_4 ................................................................................. 133
PILOT OVERVIEW .............................................................................................................................. 133 SUMMARY OF PILOT BEFORE TRIAL 2 ..................................................................................................... 133
Linked Data ......................................................................................................................... 135 PREPARATION AND EXECUTION OF TRIAL 2 .............................................................................................. 138
Trial 2 timeline .................................................................................................................... 138 Preparation for Trial 2 ......................................................................................................... 139 Trial 2 execution ................................................................................................................. 140 Trial 2 results ...................................................................................................................... 143
COMPONENTS, DATASETS AND PIPELINES ................................................................................................ 144 DataBio Component deployment status .............................................................................. 144 Data Assets ......................................................................................................................... 145
EXPLOITATION AND EVALUATION OF PILOT RESULTS .................................................................................. 146 Pilot exploitation based on results....................................................................................... 146 KPIs ..................................................................................................................................... 147
11 PILOT 9 [B2.1] MACHINERY MANAGEMENT ......................................................................................... 148
PILOT OVERVIEW .............................................................................................................................. 148 SUMMARY OF PILOT BEFORE TRIAL 2 ..................................................................................................... 148 PREPARATION AND EXECUTION OF TRIAL 2 .............................................................................................. 149
Trial 2 timeline .................................................................................................................... 149 Preparation for Trial 2 ......................................................................................................... 149 Trial 2 execution ................................................................................................................. 151 Trial 2 results ...................................................................................................................... 152
COMPONENTS, DATASETS AND PIPELINES ................................................................................................ 155 DataBio component deployment status .............................................................................. 155 Data Assets ......................................................................................................................... 157
EXPLOITATION AND EVALUATION OF PILOT RESULTS .................................................................................. 158 Pilot exploitation based on results....................................................................................... 158 KPIs ..................................................................................................................................... 160
12 PILOT 10 [C1.1] INSURANCE (GREECE) .................................................................................................. 162
PILOT OVERVIEW .............................................................................................................................. 162 SUMMARY OF PILOT BEFORE TRIAL 2 ..................................................................................................... 162
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PREPARATION AND EXECUTION OF TRIAL 2 .............................................................................................. 163 Trial 2 timeline .................................................................................................................... 163 Preparation for Trial 2 ......................................................................................................... 163 Trial 2 execution ................................................................................................................. 167 Trial 2 results ...................................................................................................................... 171
COMPONENTS, DATASETS AND PIPELINES ................................................................................................ 172 DataBio component deployment status .............................................................................. 172 Data Assets ......................................................................................................................... 173
EXPLOITATION AND EVALUATION OF PILOT RESULTS .................................................................................. 174 Pilot exploitation based on results....................................................................................... 174 KPIs ..................................................................................................................................... 175
13 PILOT 11 [C1.2] FARM WEATHER INSURANCE ASSESSMENT ................................................................ 178
PILOT OVERVIEW .............................................................................................................................. 178 SUMMARY OF PILOT BEFORE TRIAL 2 ..................................................................................................... 179 PREPARATION AND EXECUTION OF TRIAL 2 .............................................................................................. 182
Trial 2 timeline .................................................................................................................... 182 Preparation for Trial 2 ......................................................................................................... 182 Trial 2 execution ................................................................................................................. 186 Trial 2 results ...................................................................................................................... 199
COMPONENTS, DATASETS AND PIPELINES ................................................................................................ 199 DataBio component deployment status .............................................................................. 199 Data Assets ......................................................................................................................... 201
EXPLOITATION AND EVALUATION OF PILOT RESULTS .................................................................................. 202 Pilot exploitation based on results....................................................................................... 202 KPIs ..................................................................................................................................... 203
14 PILOT 12 [C2.1] CAP SUPPORT .............................................................................................................. 205
PILOT OVERVIEW .............................................................................................................................. 205 SUMMARY OF PILOT BEFORE TRIAL 2 ..................................................................................................... 205 PREPARATION AND EXECUTION OF TRIAL 2 .............................................................................................. 209
Trial 2 timeline .................................................................................................................... 210 Preparation for Trial 2 ......................................................................................................... 212 Trial 2 execution ................................................................................................................. 217 Trial 2 results ...................................................................................................................... 220
COMPONENTS, DATASETS AND PIPELINES ................................................................................................ 229 DataBio component deployment status .............................................................................. 229 Data Assets ......................................................................................................................... 233
EXPLOITATION AND EVALUATION OF PILOT RESULTS .................................................................................. 235 Pilot exploitation based on results....................................................................................... 235 KPIs ..................................................................................................................................... 236
15 PILOT 13 [C2.2] CAP SUPPORT (GREECE)............................................................................................... 238
PILOT OVERVIEW .............................................................................................................................. 238 SUMMARY OF PILOT BEFORE TRIAL 2 ..................................................................................................... 238 PREPARATION AND EXECUTION OF TRIAL 2 .............................................................................................. 239
Trial 2 timeline .................................................................................................................... 239 Preparation for Trial 2 ......................................................................................................... 240 Trial 2 execution ................................................................................................................. 241 Trial 2 results ...................................................................................................................... 246
COMPONENTS, DATASETS AND PIPELINES ................................................................................................ 247 DataBio component deployment status .............................................................................. 247
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Data Assets ......................................................................................................................... 248 EXPLOITATION AND EVALUATION OF PILOT RESULTS .................................................................................. 250
Pilot exploitation based on results....................................................................................... 250 KPIs ..................................................................................................................................... 250
16 CONCLUSION ........................................................................................................................................ 252
Table of Figures FIGURE 1: PILOT A1.1 HIGH-LEVEL OVERVIEW ............................................................................................................. 27 FIGURE 2: PILOT A1.1 TIMELINE ............................................................................................................................... 28 FIGURE 3: SCREENSHOT OF THE UNIFIED UI DEVELOPED FOR A1.1 TRIAL 2. THE RED MENU ITEM INDICATES FARM LOG FUNCTIONALITIES
WHILE THE ORANGE MENU ITEM THE FARM MANAGEMENT FUNCTIONALITIES RESPECTIVELY. .......................................... 29 FIGURE 4: SCREENSHOTS OF THE ANDROID APP USED FOR COLLECTING FARM DATA ............................................................... 30 FIGURE 5: PARCEL MONITORING AT CHALKIDIKI PILOT SITE INDICATING INTRA-FIELD VARIATIONS IN TERMS OF VEGETATION INDEX
(NDVI) AND CROSS-CORRELATIONS AMONG THE LATTER WITH: A) AMBIENT TEMPERATURE (°C) AND B) RAINFALL (MM) ..... 32 FIGURE 6: PARCEL MONITORING AT STIMAGKA PILOT SITE INDICATING INTRA-FIELD VARIATIONS IN TERMS OF VEGETATION INDEX (NDVI)
AND CROSS-CORRELATIONS AMONG THE LATTER WITH A) NDVI FROM 2018 CULTIVATING PERIOD AND B) RAINFALL (MM) FROM
2018 AND 2019 CULTIVATING PERIODS ............................................................................................................ 33 FIGURE 7: IRRIGATION MONITORING AT A VERIA PILOT PARCEL SHOWING TWO (2) CORRECT IRRIGATIONS (WATER DROP ICONS) AFTER
FOLLOWING THE ADVISORY SERVICES DURING 2019 CULTIVATING PERIOD. THE IMPACT OF RAINFALLS IN THE SOIL WATER
CONTENT IS OBVIOUS (~10/6) AND IF TRANSLATED CORRECTLY CAN PREVENT UNNECESSARY IRRIGATIONS ........................ 33 FIGURE 8: CROP PROTECTION MONITORING AT A VERIA PILOT PARCEL SHOWING FOUR (4) CORRECT SPRAYS (SPRAYING ICONS) AFTER
FOLLOWING THE ADVISORY SERVICES AND THE INDICATIONS FOR HIGH CURL LEAF RISK DURING 2019 CULTIVATING PERIOD. THE
DASHED VERTICAL LINES INDICATE CRITICAL CROP PHENOLOGICAL STAGES .................................................................. 34 FIGURE 9: FERTILIZATION ADVICE FOR A CHALKIDIKI PILOT PARCEL..................................................................................... 34 FIGURE 10: PILOT A1.1 AGGREGATED FINDINGS .......................................................................................................... 35 FIGURE 11: REPRESENTATIVES OF E.C., FARM EUROPE AND OTHER PARTICIPANTS OF THE PILOT VISIT IN STIMAGKA ..................... 39 FIGURE 12: A1.2 FIELD LOCATIONS IN 2018 MONITORING PROGRAM ............................................................................... 44 FIGURE 13: WATCHITGROW® SCREENSHOT OF THE “FIELD DASHBOARD” ........................................................................... 46 FIGURE 14: “GREENNESS” FAPAR CURVE .................................................................................................................. 47 FIGURE 15: CORRELATION BETWEEN THE HARVEST DATE FOR SUGAR BEET SEEDS IN 2019 ESTIMATED FROM SENTINEL-2 IMAGES (DATE
WITH FAPAR = 0,4) AND THE ACTUAL HARVEST DATE RECORDED BY CAC SEEDS ......................................................... 48 FIGURE 16: FAPAR VALUES AT HARVEST FOR 2019 ...................................................................................................... 48 FIGURE 17: CORRELATION BETWEEN THE HARVEST DATE FOR SUGAR BEET SEEDS IN 2018 (LEFT) AND 2019 (RIGHT) ESTIMATED FROM
FUSED SENTINEL-1 AND SENTINEL-2 IMAGES (DATE WITH CROPSAR FAPAR = 0,36) AND THE ACTUAL HARVEST DATE RECORDED
BY CAC SEEDS ............................................................................................................................................. 49 FIGURE 18: ERROR OF HARVEST DATE ESTIMATION, IN DAYS, FOR 2018 AND 2019 (138 FIELDS) ............................................ 49 FIGURE 19: CORRELATION BETWEEN THE HARVEST DATE FOR SUGAR BEET SEEDS IN 2018 (LEFT) AND 2019 (RIGHT) ESTIMATED FROM
FUSED SENTINEL-1 AND SENTINEL-2 IMAGES (CROPSAR FAPAR) ON 15 AUGUST (FULL SEASON) AND THE ACTUAL HARVEST
DATE RECORDED BY CAC SEEDS ....................................................................................................................... 50 FIGURE 20: CORRELATION (R² VALUE) BETWEEN THE ESTIMATED AND ACTUAL HARVEST DATES AT DIFFERENT TIMES BEFORE HARVEST IN
2018 (BLUE) AND 2019 (GREEN) .................................................................................................................... 51 FIGURE 21: CORRELATION BETWEEN THE HARVEST DATE FOR SUGAR BEET SEEDS IN 2019 ESTIMATED FROM (LEFT) ORIGINAL SENTINEL-
2 IMAGES (DATE WITH FAPAR = 0,23) AND (RIGHT) FUSED SENTINEL-1 AND SENTINEL-2 IMAGES (DATE WITH CROPSAR FAPAR
= 0,18) AND THE ACTUAL HARVEST DATE RECORDED BY CAC SEEDS ......................................................................... 51 FIGURE 22: ERROR OF HARVEST DATE ESTIMATION FOR SOYBEANS, IN DAYS, FOR 2019 (41 FIELDS) ......................................... 52 FIGURE 23: CORRELATION BETWEEN THE HARVEST DATE FOR SOYBEANS IN 2019 ESTIMATED FROM FUSED SENTINEL-1 AND SENTINEL-
2 IMAGES (CROPSAR FAPAR) ON 20 OCTOBER (FULL SEASON) AND THE ACTUAL HARVEST DATE RECORDED BY CAC SEEDS . 52 FIGURE 24: SUNFLOWER FIELD AT HARVESTING STAGE ................................................................................................... 53 FIGURE 25: PROCESSED SENTINEL DATA INTO GREENNESS; AVAILABLE FOR THE GROWING SEASON (A1.3) ................................. 57
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FIGURE 26: GREENNESS GRAPH DURING GROWING SEASON (A1.3) .................................................................................. 58 FIGURE 27: IMAGE DEMONSTRATING DROUGHT IN SUMMER 2018 FROM SENTINEL DATA (A1.3) ........................................... 58 FIGURE 28: ANALYSIS OF GREENLAND MANAGEMENT BASED ON THE GREENNESS FROM SENTINEL DATA (A1.3) .......................... 58 FIGURE 29: CONCEPT OF A SIMPLE (STARCH) POTATO DSS ............................................................................................. 60 FIGURE 30: MAP OF SOIL CHARACTERISTICS FOR THE NETHERLANDS.................................................................................. 61 FIGURE 31: WEATHER DATA (PRECIPITATION PER DAY VS TEMPERATURE) FROM WEATHER STATIONS ........................................ 61 FIGURE 32: WEATHER DATA (PRECIPITATION) FROM WEATHER STATIONS ........................................................................... 62 FIGURE 33: SOIL MOISTURE SENSORS ......................................................................................................................... 62 FIGURE 34: A1.3 GENERAL LOCATION ........................................................................................................................ 63 FIGURE 35: FARM AREAS SELECTED FOR THE PILOT A1.3 ................................................................................................ 63 FIGURE 36: ONLINE PLATFORM FOR CROP MONITORING AND BENCHMARKING ..................................................................... 64 FIGURE 37: LAI-WDVI POLYNOMIAL REGRESSION MODEL FOR SPRING POTATOES ACHIEVING HIGH R2. DOI: 10.1117/12.2029099
................................................................................................................................................................ 65 FIGURE 38: POTATO TRIAL FIELDS ............................................................................................................................. 66 FIGURE 39: UAV SPECTRAL IMAGE (RED EDGE NDVI -INDEX) IMAGE TAKEN 25 JUNE 2019 .................................................. 66 FIGURE 40: MONITORING OF TRIAL FIELDS DURING JULY AND AUGUST .............................................................................. 67 FIGURE 41: PERFORMANCE OF YIELD POTENTIAL (MEAN VALUES VS DATE) .......................................................................... 67 FIGURE 42: CROP MONITORING EXPRESSING VARIABILITY IN LAI ...................................................................................... 69 FIGURE 43: SOIL MOISTURE AND LAI INDEX DATA FOR THE PILOT FIELDS............................................................................. 70 FIGURE 44: PREDICTION DRY MATTER, BEGINNING OF JULY 2019..................................................................................... 71 FIGURE 45: DATA FOR THE WATER-LIMITED GROWTH MODEL .......................................................................................... 71 FIGURE 46: WATER LIMITED CROP GROWTH MODEL WITHOUT GROUNDWATER ................................................................... 72 FIGURE 47: DRY MATTER AND TOTAL YIELD FOR PILOT FIELDS DURING THE BEGINNING OF JULY AND HARVEST TIME ...................... 72 FIGURE 48: POTENTIAL CROP PRODUCTION (A1.3) ....................................................................................................... 73 FIGURE 49: A1.3 SAMPLES ..................................................................................................................................... 73 FIGURE 50: TOMATO ACCESSIONS IN GLASSHOUSE UNDER BREEDING SETTINGS .................................................................... 82 FIGURE 51: DDRAD PROTOCOL MODIFIED FROM PETERSON ET AL., 2012. PMCID: PMC3365034,
DOI:10.1371/JOURNAL.PONE.0037135 ........................................................................................................ 83 FIGURE 52: THE STACKS PIPELINE, AVAILABLE AT HTTP://CATCHENLAB.LIFE.ILLINOIS.EDU/STACKS/MANUAL-V1/....................... 83 FIGURE 53: CREA’S SORGHUM PILOT FIELDS USED IN THE C22.03 GENOMIC MODELS PLATFORM ............................................ 84 FIGURE 54: PRINCIPAL COMPONENT ANALYSIS FOR THE TOMATO POPULATIONS BASED ON THEIR GENETIC BACKGROUND ............... 91 FIGURE 55: PRINCIPAL COMPONENT ANALYSIS FOR THE TOMATO INDIVIDUALS BASED ON THEIR GENETIC BACKGROUND ................ 91 FIGURE 56: PRINCIPAL COMPONENT ANALYSIS FOR THE TOMATO INDIVIDUALS BASED ON THEIR BIOCHEMICAL BACKGROUND.......... 92 FIGURE 57: DISTRIBUTION (BOXPLOT) OF GS MODELS VALIDATED ACCURACY IN EXTERNAL SAMPLE (NOT USED DURING MODEL
TRAINING) OF 34 (30% OF THE TOTAL POPULATION) SORGHUM LINES. FEN, FLA, TAC, TAN, RESPECTIVELY, POLYPHENOLS,
FLAVONOIDS, TOTAL ANTIOXIDANT CAPACITY, AND CONDENSED TANNINS. TRAITS MEANS ARE INCLUDED WITHIN THE BOXPLOT.
TRAIT MEANS WITH SAME LETTER ARE NOT SIGNIFICANTLY DIFFERENT AT THE 5% LEVEL USING THE TUKEY'S HSD (HONESTLY
SIGNIFICANT DIFFERENCE) TEST. REFER TO TEXT FOR THE DESCRIPTION OF THE GS MODELS. ........................................... 93 FIGURE 58: PILOT B1.1 TIMELINE ............................................................................................................................. 99 FIGURE 59: KC AND NDVI EQUATIONS .................................................................................................................... 100 FIGURE 60: LEFT TO RIGHT: NDVI IMAGE FROM MULTISPECTRAL RPAS DATA; RGB MOSAIC; THERMAL IMAGE OVER RGB MOSAIC;
DSM ...................................................................................................................................................... 101 FIGURE 61: COMPARATIVE KC OBTAINS FOR REMOTE SENSOR IN FRONT FAO DATA PER CEREAL ............................................ 102 FIGURE 62: RESULT: HIGH-SCALE VIGOUR MAP .......................................................................................................... 103 FIGURE 63: CROPS CLASSIFICATION AND IRRIGATION NEEDS .......................................................................................... 104 FIGURE 64: MANAGEMENT PROFILE - IRRIGATION NEEDS OF THE WHOLE IRRIGATION COMMUNITY ........................................ 104 FIGURE 65: FARMER PROFILE - IRRIGATION NEEDS FOR A SPECIFIC PARCEL AND CROP .......................................................... 105 FIGURE 66: RASPBERRY UNIT AND IOT SENSORS ......................................................................................................... 106 FIGURE 67: DATA FLOW DIAGRAM OF THE MODEL FOR THE IMPLEMENTATION OF PRECISION AGRICULTURE TECHNIQUES ............. 107 FIGURE 68: DEFINITION OF HISTOGRAMS. RESULT OF HOMOGENIZATION OF IMAGES .......................................................... 108 FIGURE 69: PILOT B1.2 HIGH-LEVEL OVERVIEW ......................................................................................................... 114
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FIGURE 70: PILOT B1.2 TIMELINE ........................................................................................................................... 115 FIGURE 71: SCREENSHOT OF THE UNIFIED UI DEVELOPED FOR TRIAL 2. THE RED MENU ITEM INDICATES FARM LOG FUNCTIONALITIES
WHILE THE ORANGE MENU ITEM THE FARM MANAGEMENT FUNCTIONALITIES RESPECTIVELY ......................................... 116 FIGURE 72: SCREENSHOTS OF THE ANDROID APP USED FOR COLLECTING FARM DATA ........................................................... 117 FIGURE 73: PARCEL MONITORING AT KILELER PILOT SITE INDICATING SOME SLIGHT INTRA-FIELD VARIATIONS IN TERMS OF VEGETATION
INDEX (NDVI) AND CROSS-CORRELATIONS AMONG THE LATTER WITH AMBIENT TEMPERATURE AND RAINFALL (MM) ......... 118 FIGURE 74: REFERENCE EVAPOTRANSPIRATION MONITORING AT KILELER (BOTH MODELLED USING ML METHODS DEVELOPED BY NP
AND BASED ON COPERNICUS EO DATA) FOR JULY 2019 ...................................................................................... 119 FIGURE 75: IRRIGATION MONITORING AT A KILELER PILOT PARCEL SHOWING ONE (1) CORRECT IRRIGATION (WATER DROP ICON) AFTER
FOLLOWING THE ADVISORY SERVICES. THE IMPACT OF RAINFALLS IN THE SOIL WATER CONTENT IS OBVIOUS ON SEVERAL
OCCASIONS AND IF TRANSLATED CORRECTLY CAN PREVENT UNNECESSARY IRRIGATIONS ............................................... 119 FIGURE 76: AGGREGATED RESULTS OF THE PILOT IN COMPARISON WITH THE TARGET VALUES ................................................ 120 FIGURE 77: SORGHUM PILOTS ESTABLISHED IN 2019 .................................................................................................. 125 FIGURE 78: SORGHUM FOLIAR DISEASES DETECTED AREA WITH THE RELIABILITY OF 0,925 .................................................. 125 FIGURE 79: SORGHUM FOLIAR DISEASES DETECTED AREA WITH THE RELIABILITY OF 0,861 .................................................. 126 FIGURE 80: MAP OF ITALY (A) WITH A RECTANGLE INSET INDICATING THE GEOGRAPHICAL LOCATION OF THE EXPERIMENTAL SITES (RED
DOTS) FOR PILOTS ESTABLISHED IN 2017 (B) AND 2018 (C) ................................................................................ 127 FIGURE 81: LEFT: VISUALIZATION OF MODELS CROSS-VALIDATION MAE (T HA-1) DISPERSION USING BOXPLOT APPROACH AND FAPAR
ACQUIRED FROM APRIL TO AUGUST. LM, BARTMACHINE, BAYESGLM, XGBTREE, RESPECTIVELY, SIMPLE LINEAR MODEL,
BAYESIAN ADDITIVE REGRESSION TREES (BARTMACHINE METHOD), BAYESIAN GENERALIZED LINEAR MODEL (BAYESGLM
METHOD), AND EXTREME GRADIENT BOOSTING (XGBTREE METHOD). RIGHT:RELATIVE IMPORTANCE OF REGRESSORS (DAY OF
YEAR, D) ON SORGHUM BIOMASS YIELDS USING BARTMACHINE METHOD ................................................................ 130 FIGURE 82: YIELD MAPS REPRESENTED AS RELATIVE VALUES TO THE AVERAGE CROP YIELD OF EACH FIELD (HARVEST 2018) .......... 134 FIGURE 83: TRANSFORMATION AND PUBLICATION OF CZECH DATA AS LINKED DATA WITH PROTOTYPE SYSTEM FOR VISUALISING ... 135 FIGURE 84: MAP VISUALISATION PROTOTYPE (HSLAYER APPLICATION) - HTTP://APP.HSLAYERS.ORG/PROJECT-DATABIO/LAND/ ... 138 FIGURE 85: GRAPHS OF SENTINEL-2 NDVI DURING THE VEGETATION PERIOD 2019 FOR WINTER WHEAT (ABOVE) AND SPRING BARLEY
(BELLOW) AT LOCALITY OTNICE (ROSTENICE FARM). LOW PEAKS INDICATE OCCURRENCE OF CLOUDS WITHIN THE SCENE (SOURCE:
SENTINEL-2, LEVEL L1C, GOOGLE EARTH ENGINE) ............................................................................................ 139 FIGURE 86: EXAMPLE OF THE OUTPUT MAP PRODUCTS FROM YIELD POTENTIAL ZONES CLASSIFICATION FROM EO TIME-SERIES ANALYSIS:
CLASSIFICATION INTO 5% CLASSES (LEFT), 5-ZONE MAP (MIDDLE) AND 3-ZONE MAP (RIGHT). BLUE/GREEN AREAS INDICATE
HIGHER EXPECTED YIELD ............................................................................................................................... 140 FIGURE 87: MAP OF YIELD POTENTIAL ZONES (5-ZONE MAP) UPDATED FOR 2019 SEASON FROM 8-YEAR TIME-SERIES IMAGERY; FOR
SOUTHERN (LEFT) AND NORTHERN (RIGHT) PART OF ROSTENICE FARM .................................................................... 140 FIGURE 88: VARIABLE RATE APPLICATION OF SOLID FERTILIZERS BY TWIN BIN APLICATOR ON TERRAGATOR ............................... 141 FIGURE 89: VARIABLE RATE APPLICATION OF LIQUID N FERTILIZERS (DAM390) BY 36M HORSCH LEEB PT330 SPRAYER ............ 141 FIGURE 90: CROP YIELD MAPS FROM 2019 HARVEST ................................................................................................... 142 FIGURE 91: GRAPH WITH CHANGES OF CORRELATION COEFFICIENTS BETWEEN WINTER WHEAT AND SET OF SENTINEL-2 VEGETATION
INDICES DURING THE VEGETATION PERIOD 2018. MOST SENSITIVE PERIOD WAS DETECTED IN MAI AND JUNE .................. 142 FIGURE 92: GRAPH OF CORRELATION COEFFICIENTS BETWEEN WINTER WHEAT YIELD MAPS AND SENTINEL-2 NDMI (2018/06/10)
AMONG OBSERVED FIELDS. HIGHEST CORRELATION WAS DETECTED ON THE FIELDS WITH HIGHER ACREAGE AND SPATIAL
HETEROGENEITY ......................................................................................................................................... 143 FIGURE 93: TRACTOR TRAJECTORY AND WORK LOG ..................................................................................................... 148 FIGURE 94: ZETOR MAJOR .................................................................................................................................... 150 FIGURE 95: DAILY TRACTOR UTILISATION AND TRAJECTORY IN FARMTELEMETRY ................................................................ 153 FIGURE 96: SPIKES CAUSED BY 10 SECONDS INTERVAL ................................................................................................. 154 FIGURE 97: DATA COLLECTION WITH 2 SECONDS INTERVAL ........................................................................................... 154 FIGURE 98: FLUCTUATIONS IN FUEL TANK MEASUREMENT............................................................................................. 155 FIGURE 99: PILOT TIMELINE ................................................................................................................................... 163 FIGURE 100: CROP NDVI PROBABILITY DISTRIBUTION REFERRING TO A DECAD OF THE YEAR (WHEAT-LARISA REGION-2ND DECAD OF
FEBRUARY). ANOMALIES CAN BE FOUND AT THE DISTRIBUTION EXTREMES ............................................................... 164
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FIGURE 101: COTTON MODEL IN KOMOTINI REGION (T35TLF TILE, MAIZE MODEL IN EVROS REGION (T35TMF TILE) AND WHEAT
MODEL IN LARISA REGION (T34SFJ TILE) BY DECAD (HORIZONTAL AXIS) .................................................................. 165 FIGURE 102: AFTERMATH OF THE FLOODS IN KOMOTINI REGION (11/7/2019) ................................................................ 167 FIGURE 103: RAINFALL VOLUME (MM) IN THE KOMOTINI REGION .................................................................................. 168 FIGURE 104: PARCEL MONITORING AT KOMOTINI REGION (COTTON) SHOWING NEGATIVE ANOMALY (DEVIATION) FOR TWO
CONSECUTIVE DECADS JUST AFTER THE DISASTROUS INCIDENT ............................................................................... 168 FIGURE 105: HIGH-LEVEL OVERVIEW OF THE AFFECTED AREA, COLOR CODED WITH THE OUTPUT OF THE FOLLOWED DAMAGE
ASSESSMENT PROCEDURES ........................................................................................................................... 169 FIGURE 106: RISK ANALYSIS TOOL THAT MEASURES THE FREQUENCY OF PRESENCE OF EXTREME WEATHER CONDITIONS (AGAINST HEAT-
WAVES, FROSTS, OR WINDSTORMS) AS DEFINED BY ELGA .................................................................................... 169 FIGURE 107: FRAUNHOFER'S UI SCREENSHOT COLOUR CODING DIFFERENT CROP TYPES ................................................... 170 FIGURE 108: FRAUNHOFER'S UI SCREENSHOT THAT INTEGRATES CSEM’S CLASSIFICATION RESULTS INTO PIXEL HEAT MAPS...... 171 FIGURE 109: MAP CLASSIFYING THE NETHERLANDS TERRITORY IN TERMS OF NUMBER OF YEARS WITH DAMAGES ....................... 179 FIGURE 110: MAP OF PRECIPITATION EXTRACTED FROM KNMI DATASET ON DATE 30/08/2015. YELLOW POINTS: LOCATIONS
PROVIDED BY THE INSURANCE COMPANY – BLUE POINTS: FURTHER LOCATIONS WITH 24-HOURS PRECIPITATION VALUES ABOVE
THE 50 MM THRESHOLD .............................................................................................................................. 180 FIGURE 111: INTRA-FIELD ANALYSIS BASED ON NDVI SPECTRAL INDEX WITH S2A AND S2B DATA (TILE T31UET - YEAR 2018) ... 181 FIGURE 112: SENTINEL-2 TILES OVER THE NETHERLANDS ....................................................................................... 183 FIGURE 113: SPATIAL DISTRIBUTION OF POTATO FIELDS WITH RESPECT TO VARIETY FOR YEAR 2017........................................ 184 FIGURE 114: COUNT OF SAMPLES PER TYPE OF POTATOES ............................................................................................ 184 FIGURE 115: SOIL TYPE MAP .................................................................................................................................. 185 FIGURE 116: METEO CLIMATE DATA FROM LOCAL WEATHER STATIONS ............................................................................ 185 FIGURE 117: DATA FROM EO DATA SERVICE MEA .................................................................................................... 186 FIGURE 118: TEMPERATURE PROFILE (PARCEL NUMBER 1971186) ................................................................................ 186 FIGURE 119: 2016-2018 RISK MAPS (SPLIT ACROSS PAGES) ......................................................................................... 188 FIGURE 120: NVDI PER CLUSTER ............................................................................................................................ 190 FIGURE 121: PARAMETER IMPORTANCE ................................................................................................................... 191 FIGURE 122: NDVI PROFILES OF DIFFERENT TYPES OF POTATO (YEAR OF REFERENCE 2017) ................................................. 192 FIGURE 123: FIVE GROUPS OF CONSUMPTION PARCELS BASED ON CUMULATIVE TEMPERATURE BETWEEN 90 AND 200 DAY OF YEAR
.............................................................................................................................................................. 192 FIGURE 124: NDVI PROFILES OF CONSUMPTION PARCELS ACCORDING THE FIVE GROUPS IDENTIFIED BY THE TEMPERATURE ANALYSIS
.............................................................................................................................................................. 193 FIGURE 125: AVERAGE TEMPERATURE TRENDS OF PARCELS IN AREAS CHARACTERIZED BY HIGHER TEMPERATURES (BLUE) AND LOWER
TEMPERATURES (PURPLE) ............................................................................................................................. 193 FIGURE 126: FOUR GROUPS OF TBM PARCELS BASED ON CUMULATIVE TEMPERATURE BETWEEN 90 AND 200 DAY OF YEAR ....... 194 FIGURE 127: NDVI PROFILES OF TBM PARCELS ACCORDING THE FOUR GROUPS IDENTIFIED BY THE TEMPERATURE ANALYSIS ........ 194 FIGURE 128: AVERAGE TEMPERATURE TRENDS OF PARCELS IN AREAS CHARACTERIZED BY HIGHER TEMPERATURES (BLUE) AND LOWER
TEMPERATURES (RED) ................................................................................................................................. 195 FIGURE 129: THREE GROUPS OF STARCH PARCELS BASED ON CUMULATIVE TEMPERATURE BETWEEN 90 AND 200 DAY OF YEAR ... 195 FIGURE 130: NDVI PROFILES OF STARCH PARCELS ACCORDING TO THE THREE GROUPS IDENTIFIED BY THE TEMPERATURE ANALYSIS 196 FIGURE 131: FOUR GROUPS OF NAK PARCELS BASED ON CUMULATIVE TEMPERATURE BETWEEN 90 AND 200 DAY OF YEAR ........ 196 FIGURE 132: NDVI PROFILES OF NAK PARCELS ACCORDING THE FOUR GROUPS IDENTIFIED BY THE TEMPERATURE ANALYSIS ........ 197 FIGURE 133: AVERAGE TEMPERATURE TRENDS OF PARCELS IN AREAS CHARACTERIZED BY HIGHER TEMPERATURES (BLUE) AND LOWER
TEMPERATURES (RED) ................................................................................................................................. 197 FIGURE 134: INTRA-FIELD ANALYSIS BASED ON NDVI SPECTRAL INDEX WITH S2A AND S2B DATA (YEAR 2017) ........................ 198 FIGURE 135: AREAS OF ANOMALOUS GROWTH .......................................................................................................... 198 FIGURE 136: CROP FAMILIES DETECTION USING SENTINEL 2 TEMPORAL SERIES .................................................................. 206 FIGURE 137: PIXEL-BASED RESULTS OF THE ANALYSIS REGARDING POTENTIAL INCONGRUENCES WITH RESPECT TO FARMERS’
DECLARATIONS STATING CROP TYPES AND AREAS COVERED ................................................................................... 206 FIGURE 138: PILOT-BASED RESULTS OF THE ANALYSIS REGARDING POTENTIAL INCONGRUENCES WITH RESPECT TO FARMERS’
DECLARATIONS STATING CROP TYPES AND AREAS COVERED ................................................................................... 207
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FIGURE 139: NDVI TEMPORAL TREND WITH IDENTIFICATION OF RELEVANT PERIODS ........................................................... 208 FIGURE 140: TRIAL 2 TIMELINE OF ROMANIAN AOI IN PILOT C2.1 ................................................................................. 211 FIGURE 141: TRIAL 2 TIMELINE OF ITALIAN AOI IN C2.1 .............................................................................................. 212 FIGURE 142: STRUCTURE OF THE DATA FOR THE 10,000 SQKM AREA OF INTEREST ............................................................. 213 FIGURE 143: AGRICULTURAL LAND PLOTS FOR THE 10,000 SQKM AREA OF INTEREST. DATA SOURCE: AGENCY FOR PAYMENTS AND
INTERVENTION IN AGRICULTURE (APIA), ROMANIA ........................................................................................... 213 FIGURE 144: ROMANIA - TOTAL DECLARED AREA AND NUMBER OF PLOTS REGISTERED FOR CAP SUPPORT (2019). DATA SOURCE:
AGENCY FOR PAYMENTS AND INTERVENTION IN AGRICULTURE (APIA), ROMANIA .................................................... 214 FIGURE 145: LPIS CROP FAMILIES DISTRIBUTION ........................................................................................................ 215 FIGURE 146: LPIS LEGEND WITH CROP TYPE AGGREGATION IN MACRO CLASSES ................................................................. 216 FIGURE 147: SUMMARY OF MARKERS PERIODS FOR EACH MACRO CLASS OF CROP TYPE ........................................................ 217 FIGURE 148: EXAMPLES OF VERIFIED (LEFT) AND NOT VERIFIED (RIGHT) AUTUMN-WINTER ARABLE LAND PARCEL ....................... 218 FIGURE 149: EXAMPLES OF VERIFIED (LEFT) AND NOT VERIFIED (RIGHT) SUMMER ARABLE LAND PARCEL .................................. 219 FIGURE 150: EXAMPLES OF VERIFIED (LEFT) AND NOT VERIFIED (RIGHT) TEMPORARY GRASSLAND PARCEL................................ 219 FIGURE 151: EXAMPLES OF NOT VERIFIED (LEFT) AUTUMN-WINTER ARABLE LAND RE-CLASSIFIED AS SUMMER ARABLE LAND (RIGHT)
.............................................................................................................................................................. 219 FIGURE 152: EXAMPLES OF NOT VERIFIED (LEFT) SUMMER ARABLE LAND RE-CLASSIFIED AS ARTEFACT (RIGHT) DUE TO THE PRESENCE OF
A NEW BUILDING ........................................................................................................................................ 220 FIGURE 153: EXAMPLE OF CAP SUPPORT ANALYSIS - TRIAL 2 RESULTS ............................................................................ 221 FIGURE 154: TRIAL 2 RESULTS. OBSERVED CROP TYPE MAP (2019) FOR THE AREA OF INTEREST IN SOUTHEASTERN ROMANIA ...... 221 FIGURE 155: TRIAL 2 RESULTS. OBSERVED CROP TYPE MAP (2019) FOR THE ENTIRE TERRITORY OF ROMANIA .......................... 222 FIGURE 156: RESULTS OF THE VALIDATION BASED ON INDEPENDENT DATA CONSISTING OF VERY-HIGH RESOLUTION IMAGERY AND FIELD-
COLLECTED DATA ........................................................................................................................................ 223 FIGURE 157: RESULTS OF THE VALIDATION BASED ON REFERENCE DATA PROVIDED BY APIA - THE ROMANIAN NATIONAL PAYING
AGENCY ................................................................................................................................................... 224 FIGURE 158: LPIS PARCEL CLASSIFIED ACCORDING TO VERIFIED PARCELS (IN GREEN), ANOMALOUS PARCELS (IN RED) AND NOT ANALYZED
PARCELS (IN GREY) - ARABLE LAND AREA .......................................................................................................... 225 FIGURE 159: LPIS PARCELS TYPE 2016 (LEFT) AND 2018 (RIGHT) AFTER RE-CLASSIFICATION OF ANOMALOUS PARCELS - ARABLE LAND
AREA ....................................................................................................................................................... 225 FIGURE 160: 2016 LPIS SUMMER ARABLE LAND PARCELS UPDATE TO 2018 .................................................................... 226 FIGURE 161: 2016 LPIS WINTER-AUTUMN ARABLE LAND PARCELS UPDATE TO 2018 ........................................................ 226 FIGURE 162: 2016 LPIS IRRIGATED SUMMER ARABLE LAND PARCELS UPDATE TO 2018 ...................................................... 227 FIGURE 163: LPIS PARCEL CLASSIFIED ACCORDING TO VERIFIED PARCELS (IN GREEN), ANOMALOUS PARCELS (IN RED) AND NOT ANALYZED
PARCELS (IN GREY) - PERMANENT GRASSLAND AREA ........................................................................................... 227 FIGURE 164: 2016 LPIS PERMANENT GRASSLAND PARCELS UPDATE TO 2018 .................................................................. 228 FIGURE 165: EXAMPLE OF NDVI TEMPORAL TRENDS (2017-2018) OF A VINEYARD PARCEL EXPLANTED ON MARCH 2018. ........ 228 FIGURE 166: RESULTS OF THE VALIDATION BASED ON REFERENCE DATA EXTRACTED FROM VERY HIGH-RESOLUTION IMAGERY ....... 229 FIGURE 167: GEOGRAPHICAL DISTRIBUTION OF THE PARCELS THAT TAKE PART TO THE PILOT C2.2 ACTIVITIES ........................... 239 FIGURE 168: C2.2 PILOT TIMELINE ......................................................................................................................... 239 FIGURE 169: FRAUNHOFER'S UI SCREENSHOT COLOUR CODING DIFFERENT CROP TYPES ................................................... 242 FIGURE 170: FRAUNHOFER'S UI SCREENSHOT THAT INTEGRATES CSEM’S CLASSIFICATION RESULTS INTO PIXEL HEAT MAPS...... 242 FIGURE 171: NORMALIZED CROP CLASSIFICATION CONFUSION MATRIX (HORIZONTAL AXIS CORRESPONDS TO THE TRUE LABEL, WHEREAS
THE VERTICAL ONE TO THE PREDICTED LABEL) .................................................................................................... 243 FIGURE 172: GREENING ELIGIBILITY ASSESSMENT USING A TRAFFIC LIGHT SYSTEM (MAP PROJECTION EXAMPLE) ........................ 246
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List of Tables TABLE 1: THE DATABIO CONSORTIUM PARTNERS .......................................................................................................... 18 TABLE 2: OVERVIEW OF AGRICULTURE PILOT CASES ....................................................................................................... 21 TABLE 3: ADVISORY SERVICES IN PILOT A1.1. ............................................................................................................... 32 TABLE 4: MORPHOLOGICAL TRAITS OF THE PLANT, FLOWER AND LEAF IN 14 TOMATO GENOTYPES ACCORDING TO THE UPOV
GUIDELINES. ................................................................................................................................................ 87 TABLE 5: PLANT VIGOR AND TOLERANCE TO HIGH TEMPERATURES IN 14 TOMATO GENOTYPES. ................................................ 88 TABLE 6:TOTAL PRODUCTION TRAITS IN 14 TOMATO GENOTYPES (SUM OF SIX WEEKLY HARVESTS). .......................................... 89 TABLE 7: THE OBSERVED PERFORMANCE OF IMPLEMENTED MODELS. ............................................................................... 129 TABLE 8: CROP CLASSIFICATION RESULTS ................................................................................................................... 243 TABLE 9: GREENING ELIGIBILITY ASSESSMENT USING A TRAFFIC LIGHT SYSTEM. ................................................................... 245
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Definitions, Acronyms and Abbreviations Acronym /
Abbreviation Title
BDVA Big Data Value Association
BDT Big Data Technology
BRR Bayesian Ridge Regression
CAP Common Agricultural Policy
CEN European Committee for Standardization
DSS Decision Support System
EAV Entity-Attribute-Value
EO Earth Observation
ESA European Space Agency
EAGF European Agricultural Guarantee Fund
EU European Union
FAO Food and Agriculture Organisation of the United Nations
fAPAR fraction of Absorbed Photosynthetically Active Radiation
FAS Farm Advisory System
GAEC Good Agricultural and Environmental Conditions
GBLUP Genomic Best Linear Unbiased Prediction
GEOSS Group on Earth Observations
GPRS General Packet Radio Service
GS Genomic Selection
HPC High Performance Computing
IACS Integrated Administration and Control System
ICT Information and Communication Technologies
IoT Internet of Things
ISO International organization for Standardisation
JSON JavaScript Object Notation
KPI Key Performance Indicator
LAI Leaf Area Index
LASSO Least Absolute Shrinkage and Selection Operator
LPIS Land Parcel Identification System
NDVI Normalized Difference Vegetation Index
NGS Next-Generation Sequencing
NUTS Nomenclature of Territorial Units for Statistic
PC Personal Computer
PCA Principal Component Analysis
PF Precision Farming
PU Public
RPAS Remotely Piloted Aircraft System
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RTK Real Time Kinematic
SMEs Small and medium-sized enterprises
SNP Single Nucleotide Polymorhism
TRL Technology Readiness Level
UAV Unmanned Aerial Vehicle
UI User Interface
UVA, UVB (UV) ultraviolet rays, (A) long wave, (B) short wave
VRA Variable Rate Application
WP Work Package
WOFOST WOrld FOod STudies
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1 Introduction Project Summary
DataBio (Data-driven Bioeconomy) is a H2020 lighthouse project focusing on utilizing Big Data
to contribute to the production of the best possible raw materials from agriculture, forestry,
and fishery/aquaculture for the bioeconomy industry in order to produce food, energy and
biomaterials, also taking into account responsibility and sustainability issues.
DataBio has deployed state-of-the-art Big Data technologies taking advantage of existing
partners’ infrastructure and solutions. These solutions aggregate Big Data from the three
identified sectors (agriculture, forestry, and fishery) and intelligently process, analyse and
visualize them. The DataBio software environment allows the three sectors to selectively
utilize numerous software components, pipelines and datasets, according to their
requirements. The execution has been through continuous cooperation of end-users and
technology provider companies, bioeconomy and technology research institutes, and
stakeholders from the EU´s Big Data Value PPP programme.
DataBio has been driven by the development, use and evaluation of 27 pilots, where also
associated partners and additional stakeholders have been involved. The selected pilot
concepts have been transformed into pilot implementations utilizing co-innovative methods
and tools. Through intensive matchmaking with the technology partners in DataBio, the pilots
have selected and utilized market-ready or near market-ready ICT, Big Data and Earth
Observation methods, technologies, tools, datasets and services, mainly provided by the
partners within DataBio, in order to offer added-value services in their domain.
Based on the developed technologies and the pilot results, new solutions and new business
opportunities are emerging. DataBio has organized a series of stakeholder events, hackathons
and trainings to support result take-up and to enable developers outside the consortium to
design and develop new tools, services and applications based on the DataBio results.
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The DataBio consortium is listed in Table 1. For more information about the project see
www.databio.eu.
Table 1: The DataBio consortium partners
Number Name Short name Country
1 (CO) INTRASOFT INTERNATIONAL SA INTRASOFT Belgium
2 LESPROJEKT SLUZBY SRO LESPRO Czech Republic
3 ZAPADOCESKA UNIVERZITA V PLZNI UWB Czech Republic
4 FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG
DER ANGEWANDTEN FORSCHUNG E.V.
Fraunhofer Germany
5 ATOS SPAIN SA ATOS Spain
61 STIFTELSEN SINTEF SINTEF ICT Norway
7 SPACEBEL SA SPACEBEL Belgium
8 VLAAMSE INSTELLING VOOR TECHNOLOGISCH
ONDERZOEK N.V.
VITO Belgium
9 INSTYTUT CHEMII BIOORGANICZNEJ POLSKIEJ
AKADEMII NAUK
PSNC Poland
10 CIAOTECH Srl CiaoT Italy
11 EMPRESA DE TRANSFORMACION AGRARIA SA TRAGSA Spain
12 INSTITUT FUR ANGEWANDTE INFORMATIK (INFAI)
EV
INFAI Germany
13 NEUROPUBLIC AE PLIROFORIKIS & EPIKOINONION NP Greece
14 Ústav pro hospodářskou úpravu lesů Brandýs nad
Labem
UHUL FMI Czech Republic
15 INNOVATION ENGINEERING SRL InnoE Italy
16 Teknologian tutkimuskeskus VTT Oy VTT Finland
17 SINTEF FISKERI OG HAVBRUK AS SINTEF Fishery Norway
18 SUOMEN METSAKESKUS-FINLANDS SKOGSCENTRAL METSAK Finland
19 IBM ISRAEL - SCIENCE AND TECHNOLOGY LTD IBM Israel
20 WUUDIS SOLUTIONS OY2 MHGS Finland
21 NB ADVIES BV NB Advies Netherlands
22 CONSIGLIO PER LA RICERCA IN AGRICOLTURA E
L'ANALISI DELL'ECONOMIA AGRARIA
CREA Italy
23 FUNDACION AZTI - AZTI FUNDAZIOA AZTI Spain
24 KINGS BAY AS KingsBay Norway
25 EROS AS Eros Norway
26 ERVIK & SAEVIK AS ESAS Norway
27 LIEGRUPPEN FISKERI AS LiegFi Norway
28 E-GEOS SPA e-geos Italy
29 DANMARKS TEKNISKE UNIVERSITET DTU Denmark
30 FEDERUNACOMA SRL UNIPERSONALE Federu Italy
1 Replaced by partner 49 as of 1/1/2018. 2 Formerly MHG SYSTEMS OY. Terminated on 27/9/2019.
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31 CSEM CENTRE SUISSE D'ELECTRONIQUE ET DE
MICROTECHNIQUE SA - RECHERCHE ET
DEVELOPPEMENT
CSEM Switzerland
32 UNIVERSITAET ST. GALLEN UStG Switzerland
33 NORGES SILDESALGSLAG SA Sildes Norway
34 EXUS SOFTWARE LTD EXUS United
Kingdom
35 CYBERNETICA AS CYBER Estonia
36 GAIA EPICHEIREIN ANONYMI ETAIREIA PSIFIAKON
YPIRESION
GAIA Greece
37 SOFTEAM Softeam France
38 FUNDACION CITOLIVA, CENTRO DE INNOVACION Y
TECNOLOGIA DEL OLIVAR Y DEL ACEITE
CITOLIVA Spain
39 TERRASIGNA SRL TerraS Romania
40 ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS
ANAPTYXIS
CERTH Greece
41 METEOROLOGICAL AND ENVIRONMENTAL EARTH
OBSERVATION SRL
MEEO Italy
42 ECHEBASTAR FLEET SOCIEDAD LIMITADA ECHEBF Spain
43 NOVAMONT SPA Novam Italy
44 SENOP OY Senop Finland
45 UNIVERSIDAD DEL PAIS VASCO/ EUSKAL HERRIKO
UNIBERTSITATEA
EHU/UPV Spain
46 OPEN GEOSPATIAL CONSORTIUM (EUROPE)
LIMITED LBG
OGCE United
Kingdom
47 ZETOR TRACTORS AS ZETOR Czech Republic
48 COOPERATIVA AGRICOLA CESENATE SOCIETA
COOPERATIVA AGRICOLA
CAC Italy
49 SINTEF AS SINTEF Norway
Document Scope
This deliverable focuses on the results of 13 agriculture pilots after Trial 2, highlighting KPIs
(Key Performance Indicator), final outcomes, datasets processed, and tools developed.
Document Structure
This document is comprised of the following chapters:
Chapter 1 contains introduction of the project and the deliverable.
Chapters 2 offers an overview of individual tasks and pilots.
Chapters 3 to 15 are focused on individual pilots briefly introduced in chapter 2 and previously
described in deliverable D1.1 Agriculture Pilot Definition. The results of pilots include KPIs,
datasets utilisation and components overview.
Chapter 16 is the conclusion of the document.
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2 Agriculture pilots summary Overview
Work Package 1 (WP1) involves 13 agriculture pilots organised into three parallel tasks, T1.2
Precision Horticulture including vine and olives, T1.3 Arable Precision Farming, T1.4 Subsidies
and insurance. The pilots were described in the deliverable D1.2 within T1.1 task.
To enable adapting DataBio tools and services to the pilot needs and reflecting the
experiences from pilots in further development and integration of DataBio services the time
frame of the project has been divided into the following stages:
Preparatory stage was the phase where pilots and their needs were defined through a
collaborative process between Pilots Work Packages and Technical WP. During this period the
first version of DataBio platform was defined, the tools and services were adapted to the
needs of pilots in the next stage “Trial 1”. In this phase, the partners involved in pilots also
defined the first version of their business plans.
Trial 1 stage was the period where pilots were focused on using and testing the DataBio tools
and services. Those components were developed or adapted to pilot needs in the previous
preparatory stage. In addition to aiming to various technological or scientific goals, the pilots
were also focused on exploring and increasing their market potential. Deliverable D1.2 covers
these first two periods of the project.
In Trial 2 stage pilots used the updated DataBio platform and ran the second and final phase
of their experiments. In this stage, pilots were also focused on their business goals and target
markets in cooperation with WP7 (Exploitation and Business Planning) Partners.
In the final period of the DataBio project, pilots, as explained in this document, were able to
take advantage of their experience and results from the DataBio project and fully develop
their market potential.
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Introduction of pilot cases
Table 2: Overview of agriculture pilot cases
Task (topic) Subtask Pilot group Pilot
T1.2 (A) Precision
Horticulture including
vine and olives
T1.2.1 A1: Precision
agriculture in olives,
fruits, grapes and
vegetables
A1.1: Precision agriculture in
olives, fruits, grapes
A1.2: Precision agriculture in
vegetable seed crops
A1.3: Precision agriculture in
vegetables -2 (Potatoes)
T1.2.2 A2: Big Data
management in
greenhouse eco-
systems
A2.1: Big Data management
in greenhouse eco-systems
T1.3 (B) Arable
Precision Farming
T1.3.1 B1: Cereals and
biomass crops
B1.1: Cereals and biomass
crops
B1.2: Cereals and biomass
and cotton crops 2
B1.3: Cereals and biomass
crops 3
B1.4: Cereals and biomass
crops 4
T1.3.2 B2: Machinery
management
B2.1: Machinery
management
T1.4 (C) Subsidies and
insurance
T1.4.1 C1: Insurance C1.1: Insurance (Greece)
C1.2: Farm Weather
Insurance Assessment
T1.4.2 C2: CAP support C2.1: CAP Support
C2.2: CAP Support (Greece)
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Α1.1: Precision agriculture in olives, fruits and grapes (NP, GAIA, IBM, Fraunhofer)
The Greek pilot focuses on offering smart farming advisory services dedicated for the
cultivation of olives, fruits and grapes, based on a set of complementary monitoring and data
management technologies (IoT, EO data, Big Data analytics). Smart farming services comprise
irrigation, fertilization and pest/disease management advice provided through flexible
mechanisms to the farmers or the agricultural advisors. The pilot targets towards the
exploitation of heterogeneous data, facts and scientific knowledge to facilitate decisions and
their application in the field. It promotes the adoption of Big Data enabled technologies and
will collaborate with certified professionals to better manage the natural resources, optimize
the use of agricultural inputs and lead to increased product quality and yields.
Α1.2: Precision agriculture in vegetable seed crops (CAC seeds, VITO)
The Italian pilot focuses on the assessment of maturity and optimal harvest date for vegetable
seed crops using satellite images. Assessing the right time for harvesting in sugar beet seed
production is crucial to get best quality in terms of vitality and germination of the seeds
harvested. Today, the decision for starting harvesting operations in seed crops is taken by
experienced fieldsmen of CAC seeds according to empirical observations. In this pilot, satellite
observations, provided by VITO, were compared with information from the field, recorded by
CAC’s fieldsmen. From the trials in 2017, 2018 and 2019 it was found that the satellite-based
greenness index (fAPAR) derived from fused Sentinel-1 and Sentinel-2 is well suited to assess
the maturity of sugar beet seeds and a maturity model was set up to estimate the optimal
harvest date directly from the satellite images. In 2019 this info was provided in near real time
to CAC via VITO’s WatchITgrow® web application. The trials for sunflowers and soybeans were
also very promising and a similar maturity model was set up for soybeans.
A1.3: Precision agriculture in potatoes (NB Advies, VITO)
The Dutch pilot is developed by NB Advies in cooperation with VITO (Belgium). In the final
stage, the pilot will focus on farmer alerts based on growth model information and satellite
imagery. This service will provide farmers timely and automated identification of problematic
spots in potato fields, where crop growth is substantially lagging behind a certain benchmark
level. With feedback information from field visits, DSS system could combine high throughput
of field and satellite data with machine learning algorithms. Eventually, it might be able to
autonomously explain the causes of field problems to the farmers.
A2.1: Big Data management in greenhouse eco-system (CREA, CERTH)
The pilot was designed to implement Genomics Prediction Models (Genetic Selection, GS) as
a solution to technological limitations met with current breeding approaches. Phenotypic
breeding and marker-aided crop improvement have been tandemly implemented but with
good results, yet, their impact on agricultural progress has reached a plateau. Indeed, both
approaches are seriously impaired by their inability to capture the full package of genetic
factors that are at the basis of plant genetic and performance potential. GS technology
demonstrated its superiority compared to previous techniques, through its ability to capture
all information reflecting the genomic profile that breeders work with, to design technologies
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for improving quality and quantity of agricultural products. It is out of this context that this
pilot A2.1 was designed. The pilot is run by a collaborative effort between CREA (Italy) and
CERTH (Greece). Several genetic models will be implemented. The main problem modelled is
the performance of new and unphenotyped vegetable lines, integrating quantitative and
population genetics, driven by Big Data streaming from large-scale high-throughput genomic
platforms, biochemical analysis and phenotypic data. The technology is expected to
significantly improve genetic gain by unit of time and cost, allowing farmers to grow better
variety sooner relative conventional approaches, making more income.
B1.1: Cereals and biomass crops (TRAGSA-TRAGSATEC-ATOS-IBM)
The Spanish pilot is developed by TRAGSA and TRAGSATEC with the help of ATOS and IBM
Israel, also, Citoliva will participate in the final stage. This pilot develops accurate agricultural
"irrigation maps" and "vigor maps" (using EO data and sensors data as inputs) and setting up
an informative and management system for early warning of inhomogeneity. This service is a
preventive tool for farmers and landowners to avoid production loss and aims to become a
powerful system for big agricultural areas management. The final goal of the pilot is to reduce
cost for farmer communities through better exploitation and management of water and
energy resources.
B1.2: Cereals, biomass and cotton crops_2 (NP, GAIA, Fraunhofer)
The Greek pilot B1.2 focuses on offering smart farming advisory services dedicated for arable
crops (cotton cultivation), based on a set of complementary monitoring and data
management technologies (IoT, EO data, Big Data analytics). Smart farming services are
offered as irrigation advices through flexible mechanisms to the farmers or the agricultural
advisors. The pilot targets towards exploiting heterogeneous data, facts and scientific
knowledge to facilitate decisions and their applications on field. It promotes the adoption of
Big Data enabled technologies and will collaborate with certified professionals to better
manage the natural resources and specifically the use of fresh water.
B1.3: Cereal and biomass crops_3 (CREA, NOVAMONT, VITO, INFAI)
The pilot B1.3 was designed to implement remote sensing (satellite imagery, fAPAR, NDVI),
IoT farm telemetry, and proximal sensor network-based Big Data technologies for biomass
crop monitoring, predictions, and management in order to sustainably increase farming
productivity and quality, while at the same time, minimizing farming and environment
associated risks. Biomass crops of interest included biomass sorghum and cardoon, which can
be used for several bioeconomy relevant purposes (e.g. biofuel, fiber and biochemicals). The
IoT farm telemetry technology, implemented in preliminary trials and part of first trials, was
ultimately found and adapted to biomass sorghum as the hardware was susceptible to
damages induced by wild rodents or several software glitches that were harmful to pilot
operations. We are envisaging replacing IoT with VIS-NIR machine in the 2019 trials with
similar expected output in terms of analytics and technological output in support of
agricultural farming operations. The pilot secured adhesion of private farmers and/or farming
cooperatives. In collaboration with InfAI, CREA was able to extend crop monitoring to foliar
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diseases. The first results of the pilot are encouraging as there is a good agreement between
satellite data and crop phenology. The machine learning techniques showed promising
inferences with high predictive accuracy of biomass sorghum yields early on (up to 6 months
before harvesting), with important business ramifications, particularly in terms of within-
season decision support system to the parties at interest.
B1.4: Cereal, biomass and cotton crops 4 (LESP, UWB, PSNC, NB Advies)
The pilot aims to develop a platform for mapping crop vigor status by using EO data (Landsat,
Sentinel), as the support tool for variable rate application (VRA) of fertilizers and crop
protection. This includes identification of crop status, mapping of spatial variability and
delineation of management zones. The work was supported by the development of platform
for automatic downloading of Sentinel 2 data and automatic atmospheric correction.
Currently, Lesprojekt is ready to offer commercial services with processing satellite data for
any farm in Czech Republic. The pilot was also focused on transferring Czech LPIS into FOODIE
ontology and to develop effective tools for querying data. This work was done together with
PSNC and it currently supports open access to anonymous LPIS data through FOODIE ontology
and secure access to farm data.
The main focus of the pilot is to monitor cereal fields by high resolution satellite imaging data
(Landsat 8, Sentinel 2) and delineation of management zones within the fields for variable
rate application of fertilizers. The main goal is to offer farmers a solution in the form of web
GIS portal, where users can monitor their fields from EO data, based on the specified period,
select cloudless scenes and use them for further analysis. This analysis includes unsupervised
classification for defined number of classes, as identification of main zones and generating
prescription maps for variable rate application of fertilizers or crop protection products based
on the mean doses defined by farmers in web GIS interface.
B2.1: Machinery management (LESP, ZETOR, FEDERUNACOMA, PSNC)
This pilot is mainly focused on collecting telematic data from tractors and other farm
machinery to analyse and compare with other farm data. The main goal is to collect and
integrate data and receive comparable results. A challenge associated with this pilot is that a
farm may have tractors and other machinery from manufacturers that use different telematic
solutions and data ownership/sharing policies.
C1.1: Insurance (Greece) (NP, CSEM, Fraunhofer)
The main focus of the pilot is to evaluate a set of tools and services dedicated for the
agriculture insurance market that aims to eliminate the need for on-the-spot checks for
damage assessment and promote rapid payouts. The pilot concentrates on fusing
heterogeneous data (EO data, field data) for the assessment of damages at field level.
C1.2: Farm Weather Insurance Assessment (e-GEOS, NB Advies, MEEO, VITO, CSEM, EXUS)
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The objective of proposed pilot is the provision and assessment on a test area of services for
agriculture insurance market, based on the usage of Copernicus satellite data series, also
integrated with meteorological data and other ground available data. For the risk assessment
phase, the integrated usage of historical meteorological series and satellite-derived indices,
supported by proper modeling, will allow to tune EO based products in support to the risk
estimation phase. For damage assessment, the operational adoption of remotely sensed data
based services will allow optimization and tuning of new insurance products based on
objective parameters, such as maps and indices, derived from EO data and allowing a strong
reduction of ground surveys, with positive impact on insurances costs and reduction of
premium to be paid by the farmers.
Key stakeholder of the pilot is mainly the Insurance company that wants to:
• determine the regional spreading of risks for different types of bad weather event
(hail, heavy rain), to evaluate their insurance portfolio,
• determine temporal trend for different types of bad weather event (hail, heavy rain,
drought), to estimate possible influence of climate change on crop growth,
• determine the actual risk per crop on field to support the pricing of the insurance
package,
• assess the damage caused by a bad weather event, to ensure non-erroneous
compensation to farmers.
Nevertheless, farmers can be considered as secondary users and beneficiaries of the services
because they have the need to view the risk level for heavy rain and drought on field
(optionally crop specific), to evaluate the business case for prevention measures. The pilot
activities will be performed on the South of Netherlands, in an area of 1.500.000 ha targeting
at high-impact crop types.
C2.1: CAP Support (e-GEOS, Terrasigna, Tragsa)
The objective of the pilot is the provision of products and services, based on specialized highly
automated processors processing Big Data, in support to the CAP and relying on multi-
temporal series of free and open EO data, with focus on Copernicus Sentinel 2 data. Products
and services will be tuned in order to fulfil requirements from the 2015-20 EU CAP policy and
will include general information layers and indicators on EU territory with different level of
aggregation and detail up to farm level.
The proposed pilot project has been tailored on the specific needs of two end users, one
operating at National level (Romania Agriculture Ministry), and the other operating at
Regional level (AVEPA Paying Agency) which is one of the most important agricultural regions
in Italy. Services provided by the pilot will rely on the processing of Big Data, such as those
provided by Copernicus Sentinel-1 and Sentinel-2 satellite, collecting SAR and multispectral
image data with a 10-days frequency (the frequency will be increased to 5-days, when the full
constellation Sentinel-2A Sentinel-2B Sentinel 1B will be fully available).
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The pilot services will demonstrate the implementation of functionalities that could be used
for supporting the subsidy process in verifying specific requests set by the EU CAP. In
particular, services in support to the control of direct payments for the improvement use of
natural resources will be addressed. In fact, to receive decoupled green payment per ha,
farmers must fulfil specific criteria, e.g. crop diversification.
Through the subsidy collection process, the compliance of agricultural parcels usage must be
verified according to the farmers’ declaration. Therefore, services will:
• Identify different crops present inside a single farm when the global size of declared
surface is exceeding 10 ha. This is due to the fact that CAP requires crops
diversification such that farmers should cultivate at least two to three different crops.
The service will be based on the management of optical satellite data together with
farmer declaration information and limited ground measures if any and will provide
an indication of compliance/not compliance of the farmer.
• Identify parcels (monitoring objects) over which the declared crop is different from
the one that extracted from the EO models (outliers). The service is based on Sentinel
data and machine learning methods for the description of the crop and analytic
methods for the identification of the outliers. The service will allow the performing of
Big Data analytics to various crop indicators on parcel level.
C2.2: CAP Support (Greece) (NP, GAIA, CSEM)
This Greek pilot C2.1 is targeting towards the evaluation of a set of EO-based services
designed appropriately to support specific needs of the CAP value chain stakeholders. The
pilot services rely on innovative tools and complementary technologies that will sustain the
interconnection with IoT infrastructures and EO platforms, the collection and ingestion of
spatiotemporal data, the multidimensional deep data exploration and modelling and the
provision of meaningful insights, thus, supporting the simplification and improving the
effectiveness of CAP. The pilot activities aim to support the farmer during the submission of
aid application and more specifically leading to an improved “greening” compliance. The
ambition of the current pilot is to deal effectively with CAP demands for agricultural crop type
identification, systematic observation, tracking and assessment of eligibility conditions over a
period fully aligned with the main concepts of the new EU agricultural monitoring approach.
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3 Pilot 1 [A1.1] Precision agriculture in olives, fruits, grapes
Pilot overview
The main focus of this pilot is to offer smart farming advisory services referring to the
cultivation of olives, fruits and grapes, based on a set of complementary monitoring and data
management technologies (IoT, EO data, Big Data analytics). Smart farming services comprise
irrigation, fertilization and pest/disease management advices and they are provided through
flexible mechanisms to the farmers or the agricultural advisors. The pilot targets towards
exploiting heterogeneous data, facts and scientific knowledge to facilitate decisions and field
applications. It promotes the adoption of Big Data enabled technologies and collaborates with
certified professionals to better manage the natural resources, optimize the use of
agricultural inputs and lead to increased product quality and yields. NP is leading the pilot
activities with the support of GAIA EPICHEIREIN, IBM and Fraunhofer for the execution of the
full lifecycle of the pilot. The pilot activities are being performed in three (3) pilot sites in
Greece, namely Chalkidiki (olive trees) – 600ha, Stimagka (grapes) – 3000ha and Veria
(peaches) – 10000ha (Figure 1).
In order to support the business expansion of the Big Data enabled technologies that are
introduced within the present DataBio pilot, NP and GAIA EPICHEIREIN have already
established an innovative business model that allows a swift market uptake. With no upfront
infrastructure investment costs and a subscription fee proportionate to a parcel’s size and
crop type, each smallholder farmer, can now easily participate and benefit from the
provisioned advisory services. Moreover, and as more than 70 agricultural cooperatives are
shareholders of GAIA EPICHEIREIN, it is evident that there is a clear face to the market and a
great liaison with end-user communities for introducing the pilot innovations and promoting
the commercial adoption of the DataBio’s technologies.
Figure 1: Pilot A1.1 high-level overview
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Summary of pilot before Trial 2
The pilot has completed the first round of trials during Trial 1. It effectively demonstrated how
Big Data enabled technologies and smart farming advisory services can offer the means for
better managing the natural resources and for optimizing the use of agricultural inputs. All
these assumptions have been validated through a set of pilot KPIs which in their majority met
(and in some cases even exceeded) the targeted expectations (documented in D1.2). This has
been achieved as farmers and the agricultural advisors showed a collaborative spirit and
followed the advices that were generated by DataBio’s solutions. As multiple parameters
(climate and crop type related) are affecting the agricultural production it has been proven
that a solution “one-fits-all” is not applicable and several factors need to be taken into
consideration in translating the trial results (e.g. biennial bearing phenomenon in olive trees,
heavy seasonal/regional rains, multi-year fertilization strategies, etc.).
Preparation and execution of Trial 2
Trial 2 timeline
The following roadmap applies for all three (3) pilot sites (cultivation of olives in Chalkidiki,
cultivation of grapes in Stimagka, cultivation of peaches in Veria) of this pilot (Figure 2).
Figure 2: Pilot A1.1 timeline
Preparation for Trial 2
The following work was conducted by NP, as part of the preparatory work for Trial 2. As the
requirements in terms of sensors deployed for in-the-field usage differ between pilot sites, it
became obvious that several adaptations were necessary in respect to C13.03 and the way
data were represented for both cloud-based storing and Gaiatron station configuration. More
specifically, all relational and EAV (Entity-Attribute-Value) data representations were adapted
to more flexible and scalable JSON format (JavaScript Object Notation) that performs better
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in a dynamic IoT measuring environment. The latter is widely acknowledged as JSON has
become gradually the standard format for collecting and storing semi-structured datasets
originating from IoT devices. The adaptation to a JSON format for modelling IoT data streams
allows further processing, parsing, integration and sharing of data collections in support of
system interoperability, through the adaptation on well-established and favoured linked-data
approaches (JSON-LD3).
User Interface integration was performed so that the farm management portal (holding all
data of agronomic value and the embedded DSS serving as the endpoint for providing the
advisory services) is integrated with the farm electronic calendar (the endpoint where the
farmer or the agricultural advisor ingests information to the system regarding the applied
cultivation practices, field level observations, sampling, etc.). Both these tools were
developed using the component C13.01. Integration activities were conducted in order to
offer a seamless user experience and allowing the user to carry out his/her intended
operations without going back and forth across different systems.
Figure 3: Screenshot of the unified UI developed for A1.1 Trial 2. The red menu item indicates farm log functionalities while the orange menu item the farm management functionalities respectively.
A new mobile application was developed, namely “gaiasense Field Collect”, so that field-level
data collection can be performed through an Android-powered device. Lessons-learnt from
Trial 1 indicated that by using portable smart devices, would be easier for the farmer or the
agricultural advisor to ingest data into the system (farm and eye data dimensions as indicated
in Figure 1). The application was implemented with the purpose of supporting several
functionalities, presented in Figure 4, like:
1. detailed planning and control of the process of trapping and monitoring of the
population and the spread of insect infestation within a crop. Specifically, farmers
3 https://json-ld.org/
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have the ability to record insect infestation directly on the field with the help of a
smartphone and use these data to more effectively control the damage caused by
enemies while reducing the amount of insecticides released into the soil,
2. recording of the phenological stage of the cultivation at the time of the field
inspection,
3. recording of soil samples from points within the field, irrigation measurements, and
cultivation symptoms mainly from enemies and diseases.
Figure 4: Screenshots of the android app used for collecting farm data
An extension to the first event driven implementation has been performed by IBM,
accommodating one (1) more additional model/rule for peaches disease monitoring. In total
the current implementation monitors one (1) pest and one (1) disease breakout from each
pilot site (6 scientific crop protection models used in total), namely:
• spilocaea oleaginea and bactocera olea (olives)
• downy mildew and lobesia botrana (grapes)
• grapholita_molesta and curl leaf (peaches)
PROTON is performing a sophisticated temporal analysis exploiting the numerical output (risk
indicator) of GAIA Cloud’s SmartFarm services that calculate the risk associated with diseases
and pests’ breakouts using raw sensor measurements. PROTON results are being send via
email back to NEUROPUBLIC at specified intervals (e.g. once a week) for integration and
evaluation. PROTON’s running instance has been moved for Trial 2 to new and more stable
dockerised endpoints and server infrastructure by IBM.
The preparatory work conducted by FRAUNHOFER for Trial 2 concentrated on the discussion
how existing analytic services could be integrated into a web-based analytic-platform with
ease. The starting point for this was provided by the solution developed during Trial 1. During
this discussion, a variety of ideas were developed how different services could be integrated
into a single platform, which is also able to cope with multiple data sources, to fulfil the needs
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of specific use-cases. One of the major challenges of such efforts is to reduce the complexity
of integration. Principles of modern architecture styles such as Self-contained Systems (SCS)
(https://scs-architecture.org) or Microservices were considered to promote the separation
into independent components. Each component to consists of capabilities to access data,
process or analyse it and consequently visualize the result. The integration itself must be done
at the UI-Layer. Following this approach provides more flexibility and eventually allows to
think about a platform which enables the users to build views for custom analytics tasks
composed by a variety of components. The horizontal impact of this stage can provide
solutions for multiple scenarios spanning from Smart Farming, to CAP support and Agri-
Insurance.
The implementation of Trial 2 focuses primary on the integration of external services. A
variety of visual analytic tools are included to allow efficient exploration of available data. The
integration of services and data sources is done using well-defined RESTful interfaces.
Trial 2 execution
During Trial 2 the following actions have been performed by the partners involved in the pilot
activities:
By M26, the growing season starts for all pilot sites. Moreover, DataBio platform v2 for the
pilot is fully operational and involves:
• Offering to the farmers and the agricultural advisors technological tools (unified UI
and “gaiasense” field collect android app) so as to provide feedback, measurements,
samplings (e.g. soil sampling for the fertilization advices), observations about pests,
diseases and detailed data regarding the farming practices. Especially, with respect to
the farming practices information needs to be ingested into the system at regular
intervals (once a week). As the farming ecosystem is complex, it is necessary to
capture this information in detail, in order to shape a holistic view of the monitored
parcels. NP was in charge of supervising the data collection process. Moreover,
certified agricultural advisors are starting to use the aforementioned main pilot UIs in
order to access the full set of collected data (in situ agro-climate, EO-based,
crowdsourced, modelled, machine-generated), evaluate it and offer data-driven
advices to the farmers towards better resource management, improved products and
yields (more descriptions and figures can be also found in Deliverable D1.2). In total,
the advisory services provided for all three (3) pilot sites are shown in Table 3.
• PROTON starts processing the numerical output of NP’s GAIA SmartFarm services that
is essential a risk indicator against specific pest/disease breakouts for offering even
earlier alerting/warning before conditions reach critical states.
Indicative figures from the pilot sites can be found in Figures 5 - 9.
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Table 3: Advisory services in pilot A1.1.
Chalkidiki Pilot (Olives) Veria Pilot (Peaches) Stimagka Pilot (Grapes)
Irrigation + + +
Fertilization + + -
Crop
Protection
+
(exploiting scientific
models against 1 pest
and 1 disease)
+
(exploiting scientific
models against 3 pests
and 4 diseases)
+
(exploiting scientific
models against 2 pests
and 3 diseases)
Figure 5: Parcel monitoring at Chalkidiki pilot site indicating intra-field variations in terms of vegetation index (NDVI) and cross-correlations among the latter with: a) ambient temperature (°C) and b) rainfall (mm)
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Figure 6: Parcel monitoring at Stimagka pilot site indicating intra-field variations in terms of vegetation index (NDVI) and cross-correlations among the latter with a) NDVI from 2018 cultivating period and b) rainfall (mm) from 2018 and 2019 cultivating periods
Figure 7: Irrigation monitoring at a Veria pilot parcel showing two (2) correct irrigations (water drop icons) after following the advisory services during 2019 cultivating period. The impact of rainfalls in the soil water content is obvious (~10/6) and if translated correctly can prevent unnecessary irrigations
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Figure 8: Crop protection monitoring at a Veria pilot parcel showing four (4) correct sprays (spraying icons) after following the advisory services and the indications for high curl leaf risk during 2019 cultivating period. The dashed vertical lines indicate critical crop phenological stages
Figure 9: Fertilization advice for a Chalkidiki pilot parcel
By M28, a preliminary architecture for FRAUNHOFER’s analytics platform has been drafted.
The platform was the main discussion topic during the M28 DataBio Thessaloniki Codecamp,
hosted in NEUROPUBLIC’s N.Greece offices with the participation of other DataBio partners
involved in the WP1 pilots led by NEUROPUBLIC. Furthermore the generalization and simple
adaption to other scenarios was discussed intensively.
By M34, the growing season ends at all pilot sits and final KPI measurements are collected.
More specifically:
• 35 reports have been sent in total from IBM to NEUROPUBLIC offering PROTON’s CEP
results during the growing season. These reports were sent in regular intervals (once
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a week) and provided flags about pest/disease breakouts in the pilot areas. As trained,
the system provides warnings from ~1.5 to ~4 days before the original alarm for pest
breakout and several hours before the alarm for disease breakout. These warnings
were evaluated by certified agricultural advisors and contributed to the decision-
making process regarding crop protection.
• With regular discussions with the farmers and the agronomists/agricultural advisors
involved in the pilot activities, final KPI measurements and feedback were collected
and can be found in Section 3.5.2. This work was conducted by NP and GAIA
EPICHEIREIN.
Trial 2 results
In Trial 2, the applied technologies and pipelines got even more mature and reached their
expected TRL (Technology Readiness Level). The farmers and their agricultural advisors
continued (for a second year) to benefit from irrigation, fertilization and pest/disease
management advices aiming to facilitate the decision-making process and optimize the use
of agricultural inputs. The collected KPIs validated the pilot assumptions. The aggregated
results of the pilot’s Trial 2 are outlined in the Figure 10.
Figure 10: Pilot A1.1 aggregated findings
It is effectively shown that in certain cases (irrigation) the results exceeded the initial set
targets for input cost reduction. This is due to the fact that the farmers both: a) showed
collaborative spirit and adapted their farming practices using all advice offered and b) were
benefiting from the weather conditions (rainfalls during June, July 2019) and this reduced the
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fresh water requirements during critical phenological stages. The aforementioned
phenomenon, was the underlying reason for slightly not reaching the targeted crop
protection goals. The farmers chose to conduct additional proactive sprays for securing their
production against threatening situations (e.g. fruit mucilage presence at the stage of swelling
in Veria pilot site). In terms of fertilization, the exhibited deviation (under-fertilization) is part
of the farmers’ overall strategy that derives from the fact that fertilization advices are offered
with a two-to-three-year application window. This allows them a window for taking
fertilization measures and is expected that this deviation will be acknowledged and
significantly shape the fertilization strategy over the next cultivating periods.
Components, datasets and pipelines
DataBio component deployment status
Component code
and name
Purpose for pilot Deployment status Component
location
C13.01
Neurocode (NP)
Neurocode allows
the creation of the
main pilot UIs in
order to be used
by the end-users
(farmer,
agronomists) and
offer smart
farming services
for optimal
decision making
deployed NP Servers
C13.03 GAIABus
DataSmart Real-
time streaming
Subcomponent
(NP)
Real-time data
stream monitoring
for NP’s GAIAtrons
Infrastructure
installed in all
three pilot sites
Real-time
validation of data
Real-time parsing
and cross-checking
deployed NP Servers
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C19.01 Proton
(IBM)
Early warning
system for
pest/disease
management
using temporal
reasoning
(PROTON) for
olives, grapes and
peaches
deployed IBM’s lnx-
blue.sl.cloud9
.ibm.com
C04.02 – C04.04
Georocket,
Geotoolbox,
SmartVis3D
(Fraunhofer)
Back-end system
for Big Data
preparation,
handling fast
querying and
spatial
aggregations (data
courtesy of NP)
Front-end
application for
interactive data
visualization and
analytics
deployed Fraunhofer
Servers
Data Assets
Data Type Dataset Dataset
original source
Dataset
locatio
n
Volum
e (GB)
Velocity
(GB/year)
Sensor
measuremen
ts (numerical
data) and
metadata
(timestamps,
sensor id,
etc.)
Gaiasense
field. Dataset
composed of
measuremen
ts from NP’s
telemetric
IoT agro-
climate
stations
called
GAIATrons
NEUROPUBLIC GAIA
Cloud
(NP’s
servers)
Severa
l GBs
Configurable
collection and
transmission
rates for all
GAIATrons.
>20
GAIAtrons
fully
operational at
the pilot sites
collecting >
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for the pilot
sites.
30MBs of data
per year each
with current
configuration
(measuremen
ts every 10
minutes)
EO products
in raster
format and
metadata
Dataset
comprised of
remote
sensing data
from the
Sentinel-2
optical
products (5
tiles)
ESA
(Copernicus
Data)
GAIA
Cloud
(NP’s
servers)
>6000 >1900
Exploitation and Evaluation of pilot results
Pilot exploitation based on results
NP and GAIA EPICHEIREIN have already launched on 2013 their Smart Farming program, called
“gaiasense” (http://www.gaiasense.gr/en/gaiasense-smart-farming), which aims to establish
a national wide network of telemetric stations with agri-sensors and use the data to create a
wide range of smart farming services for agricultural professionals.
Within DataBio, the quality of the provided services greatly benefited from the collaboration
with leading technological partners like IBM and Fraunhofer, which specialize in the analysis
of Big Data. Moreover, feedback from the end-users and lessons-learnt from the pilot
execution significantly fine-tuned and will continue to shape the suite of dedicated tools and
services, thus, facilitating the penetration of “gaiasense” in the Greek agri-food sector.
The success of the pilot was established by high profile events4 (Figure 11) and online articles5
that were promoting the findings of the pilot and consequently the wider adoption of Big Data
enabled smart farming advisory services in the next years.
The sustainability of NP’s DataBio-enhanced smart farming services, after the end of the
project is achieved through: a) the commercial launch and market growth of “gaiasense” and
b) the participation to other EU and national R&D initiatives. This will allow continuously
evolving/validating the outcomes of the project, by working with both new and existing (to
4 http://www.gaiasense.gr/en/a-greek-innovation-gaiasense-evolves 5 https://www.ypaithros.gr/en/yannis-olive-grove-reduction-by-30-in-production-costs-and-parallel-increase-of-sales/
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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DataBio) user communities and applying its innovative approach to new and existing (again
to DataBio) areas/crops.
Figure 11: Representatives of E.C., Farm Europe and other participants of the pilot visit in Stimagka
KPIs
KPI
short
nam
e
KPI
description
Goal
descripti
on
Base
value
Target
value
Measur
ed value
Uni
t of
val
ue
Comment
A1.1
_1
Reduction
in the
average
cost of
spraying
per hectare
for the
three (3)
crop types
following
the
advisory
services at
a given
period.
Chalkidiki
(olive
trees):
250,
Stimagka
(grapes):
990,
Veria
(peaches)
: 810
Chalkidi
ki (olive
trees):
213,
Stimagk
a
(grapes)
: 955,
Veria
(peache
s): 770
Chalkidi
ki (olive
trees):
219,
Stimagk
a
(grapes)
: 963,
Veria
(peache
s): 781
eur
os/
ha
As a
consequen
ce of the
rainy June
and July
2019
months in
Greece,
proactive
sprays
were
conducted
to treat
mainly
fungal
diseases
(for
example in
Veria,
peaches
were
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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sprayed at
that time
mainly to
treat fruit
mucilage
at the
stage of
swelling)
A1.1
_2
Reduction
in the
average
number of
unnecessar
y sprays per
farm for
the three
(3) crop
types
following
the
advisory
services at
a given
period.
Chalkidiki
(olive
trees): 5
Stimagka
(grapes):
4
Veria
(peaches)
: 4
Chalkidi
ki (olive
trees): 1
Stimagk
a
(grapes)
: 1
Veria
(peache
s): 1
Chalkidi
ki (olive
trees):
1.4,
Stimagk
a
(grapes)
: 1.8,
Veria
(peache
s): 1.6
nu
mb
er
of
spr
ays
A1.1
_3
Reduction
in the
average
cost of
irrigation
per hectare
for the
three (3)
crop types
following
the
advisory
services at
a given
period.
Chalkidiki
(olive
trees):
330,
Stimagka
(grapes):
3030,
Veria
(peaches)
: 870
Chalkidi
ki (olive
trees):
230,
Stimagk
a
(grapes)
: 2130,
Veria
(peache
s): 610
Chalkidi
ki (olive
trees):
198,
Stimagk
a
(grapes)
: 2007,
Veria
(peache
s): 497
eur
os/
ha
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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A1.1
_4
Reduction
in the
amount of
fresh water
used per
hectare
following
the
advisory
services at
a given
period
Chalkidiki
(olive
trees):
817,
Stimagka
(grapes):
1868,
Veria
(peaches)
: 1703
Chalkidi
ki (olive
trees):
572,
Stimagk
a
(grapes)
: 1308,
Veria
(peache
s):1192
Chalkidi
ki (olive
trees):
492.4
Stimagk
a
(grapes)
: 1,232
Veria
(peache
s):
971.18
m3/
ha
Α
significant
reduction
in the cost
of
irrigation
has been
witnessed
that came
because of
the
farmers
following
the offered
Big Data
enabled
advisory
services
and of the
many and
heavy
rainfalls of
June and
July 2019
A1.1
_5
Reduction
in the
nitrogen
use per
hectare
following
the
advisory
services at
a given
period
Chalkidiki
(olive
trees):
230,
Veria
(peaches)
: 220
Chalkidi
ki (olive
trees):
210,
Veria
(peache
s): 140
Chalkidi
ki (olive
trees):
161
Veria
(peache
s): 61.83
kg/
ha
Α1.1
_6
Quantify %
divergence
in the cost
of the
applied
Chalkidiki
(olive
trees): -
40 (under
fertilizati
Chalkidi
ki (olive
trees): -
14,
Veria
Chalkidi
ki (olive
trees): -
11.27
%/h
a
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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fertilization
strategy
compared
to best
practices
per hectare
(agronomis
t advice)
on), Veria
(peaches)
: +20
(peache
s): +7
(under
fertilizat
ion)
Veria
(peache
s): - 44
A1.1
_7
Increase in
production
Chalkidiki
(olive
trees):
10375,
Stimagka
(grapes):
17117,
Veria
(peaches)
: 49825
Chalkidi
ki (olive
trees):
11205,
Stimagk
a
(grapes)
: 18436,
Veria
(peache
s):
53811
Chalkidi
ki (olive
trees):
7,010
Stimagk
a
(grapes)
: 18,011
Veria
(peache
s):
52,044
kg/
ha
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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4 Pilot 2 [A1.2] Precision agriculture in vegetable seed crops
Pilot overview
The pilot’s main goal is to monitor the maturity of seed crops of different species with satellite
imagery to support the decision making of farmers and fieldsmen in assessing maturity of
seed crops and optimal harvesting time in order to achieve maximum quality of their
production. On-site observation of crop development and harvest date will be matched with
information derived from satellite images.
Summary of pilot before Trial 2
Preparatory Stage
In first growing season (2017) the crop that was monitored was the sugar beet for seed
production, with the aim to tune EO with “in situ” crop monitoring and establish a
correspondence between the empiric assessment and the parameters derived from the
satellite sensors. In case of positive feedback, the trial would be expanded to a wider range
of seed crops in the next stage.
In May 2017 five sugar beet fields (14,79 hectares in total) located in the Region Emilia
Romagna were selected by CAC seeds for the purpose of the trial. To monitor the fields under
the scope of this project the web application WatchITgrow® was used. This application was
initially developed by VITO for potato monitoring and yield prediction in Belgium and adapted
in DataBio WP5 to be able to monitor other crops (sugar beets in this case) in other regions
(Italy).
Crop monitoring was performed with Sentinel-2 satellite images. From Sentinel-2 satellite
data “greenness” maps of the target-fields were derived throughout the season. These
“greenness” maps are actually showing the fraction of absorbed photosynthetically active
radiation, a measure of the crop’s primary productivity. fAPAR is often used as an indicator of
the state and evolution of crop cover. Low fAPAR values indicate that there is no crop growing
on the field (bare soil, fAPAR=0). When the crop emerges, the index will increase until the
crop has reached the maximum growing activity (fAPAR=95-100%); then its values will
decrease again until harvest. From this “crop growth curve”, information on phenology and
crop development can be retrieved and a model can be designed to decide on the right
moment for harvesting.
The results of the first trials were promising:
• Differences in maturity between sugar beet fields and variability within individual
fields were well visible from satellite greenness index maps.
• Analysis of the growth curve and discussions with the fieldsmen made CAC seeds and
VITO confident that the greenness index can be used to check when the sugar beet
seeds are ready to be harvested.
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Based on these promising results for sugar beets it was decided to extend the EO and the in
situ monitoring in the growing season 2018 to a larger number of sugar beet seed production
fields and to include new seed crops into the trial.
Trial 1 execution and results
In 2018 the EO and field monitoring was extended to approximately 90 fields of seed crops
(Figure 12). The main part of these fields were sugar beet fields. The scope of the sugar beet
monitoring in 2018 was to confirm the correlation between the fAPAR “greenness” index and
seed maturity, which appeared to be rather confident in the preliminary stage in 2017.
Furthermore, the observation was extended to several other seed crops to assess if the index
could be used to assess the right maturity stage and consequent harvesting operations
instead of the empirical methods used by farmers or the experience of the fieldsmen.
Figure 12: A1.2 field locations in 2018 monitoring program
The following crops were monitored in 2018:
• sugar beets – 61 fields
• onion – 5 fields, located in two different Provinces with different environmental
conditions
• cabbage – 5 fields, located same as onions
• sunflower – 16 fields, located in the same area as sugar beet
• alfa alfa – 3 fields
• soybean – 2 fields
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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While monitoring these fields, especially onion and cabbage which are early maturing,
problems were encountered, due to the unpredictable weather conditions in production
areas during late spring and early summer. The high number of cloudy days prevented the
fieldsmen to have access to the images during their field checks; hence, their reports and
checks were not influenced by the satellite data.
For cabbage the index is much more difficult to match with the harvesting dates decided by
fieldsmen. The curve gets its peak during the winter and decreases until the outset of
blooming in spring. Besides, different curves from different fields and different areas were
acquired. Probably the index is affected by plant density – for which there are different
recommendations according to the variety – and by differences in the ratio between female
and male lines (note that the male lines are destroyed after the flowering).
Concerning onion fields similar problems as for cabbage were encountered: high
heterogeneity of the greenness curves with respect to the harvesting dates decided by
fieldsmen was acquired.
For the reasons these two species were excluded from EO program in Trial 2.
The greenness curves resulting from the monitoring of the sunflower fields appeared more
reliable. The studied index followed closely the growth of the plants and tended to replicate
in all fields that were monitored. Harvesting of sunflower seeds was generally postponed for
a few days after the greenness index reaches its minimum at the end of August - September
(decreasing part of the greenness curve). This corresponds to the actual field practices: for
sunflowers harvesting operations are carried out after the seed maturity. The reason is that
the plants are left to dry in the field before they are placed into the combine, in order to ease
the threshing operations and to easily separate the seed from the heads.
Two fields of soybeans were introduced in the pilot, as they were close to the monitored fields
of sugar beet and sunflower. The resulting fAPAR curves appeared to be quite reliable and it
was decided to monitor this crop at a larger scale in Trial 2 and set up a model for estimating
the optimal harvest date according to the fAPAR index.
Overall, 61 fields of sugar beet were monitored in 2018. From the comparison of the fAPAR
curves and the harvesting dates assessed by fieldsmen, it was found that the average fAPAR
value at harvest was 0,39.
While field assessment had been carried without controlling the index, not all fields were
harvested at the exact index value. The germination rate of the seed lots harvested was
compared to the harvesting date, to check if harvesting at lower or higher fAPAR values –
especially for those lots harvested in advance – is correlated with a difference in germination;
yet, no significant differences were observed.
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Preparation and execution of Trial 2
Trial 2 timeline
Trial 2 covered the 2019 growing season.
Preparation and execution of Trial 2
Trial 2 focused on crops which in Trial 1 and in the preparatory stage showed reliable response
to seed maturity assessment parameters derived from satellite data. Sugar beets, sunflower
and soybean were the crops monitored during season 2019, from sowing/transplanting until
harvesting.
As in Trial 1, fields were periodically monitored on site by the fieldsmen, which reported the
main growing stage of each crop and assessed the timing for harvesting in the traditional way.
The same fields were monitored with EO through the web application WatchITgrow®
developed by VITO (Figure 13).
Figure 13: WatchITgrow® screenshot of the “field dashboard”
The fields monitored were localised on the map and a field polygon was drawn by fieldsmen
according to on-site inspection. The application also allowed adding field data reported by
fieldsmen during their periodical visits.
The scale of monitoring was increased for sugar beet (aprox. 250 ha monitored) and for
soybean (aprox. 600 ha monitored), while for sunflower it was restricted to a few fields just
to tune the curve to the real seed maturity.
For each crop the field data were combined with satellite data to set up a robust model for
seed maturity assessment.
Trial 2 results
In 2019 CAC seeds monitored 77 sugar beet fields and 41 soybean fields with WatchITgrow®
application. Sentinel-2 satellite images provided information on the greenness and health of
the crops. From the greenness (fAPAR) curves (Figure 14) the optimal harvest date of the
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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sugar beet and soybean seeds was estimated in near realtime, using the maturity model
developed in 2018.
Figure 14: “Greenness” fAPAR curve
The objectives for the 2019 season were:
• for sugar beets:
o To validate the maturity model which was trained with data from 2018 with
data from 2019
o To use fused Sentinel-1 and -2 satellite images as input for harvest date
estimation and check the impact on the accuracy of the harvest date estimates
o To check the performance and the forecasting ability of the maturity model by
determining the accuracy of the harvest date forecasts at different moment
during the harvesting period
• for soybeans:
o To develop a maturity model for this crop, similar to the model developed for
sugar beets.
Results for sugar beets
Test 1: validate the approach for maturity assessment using the 2019 dataset
From the analysis of the 2018 dataset it was found that the “optimal harvest date”
corresponded to the date in the period at which the Sentinel-2 derived fAPAR reaches 0,4.
Figure 15 shows the estimated vs. actual harvest date for the sugar beets fields that were
monitored in 2019. Compared to 2018 the correlations are much lower in 2019 (R² = 0,20 vs
R² = 0,78 in 2018). This can partly be explained by the growing conditions in the summer of
2019 which were not optimal.
The fAPAR values at harvest showed a much larger variation in 2019 (Figure 16).
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Figure 15: Correlation between the harvest date for sugar beet seeds in 2019 estimated from Sentinel-2 images (date with fAPAR = 0,4) and the actual harvest date recorded by CAC seeds
Figure 16: fAPAR values at harvest for 2019
Test 2: use of improved fAPAR time series
In summer 2019 the weather conditions at harvesting (July) were not optimal as there were
a lot of cloudy days. Optical satellites such as Sentinel-2 are unable to look through clouds.
This resulted in cloud-induced gaps in observations. When cloud free observations are lacking
for several weeks interpolation or smoothing techniques cannot bring a solution anymore.
The CropSAR technology developed by VITO provides a way to keep on monitoring crop
growth and development, independent of weather conditions. CropSAR relies on
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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observations made by Sentinel-1, a constellation of two radar satellites. Even though optical
and radar sensors see completely different things, their measurements are nevertheless
correlated, as both hold information on the vegetation status. It is exactly that correlation
that CropSAR exploits to fill in the cloud-induced gaps in Sentinel-2’s optical measurements.
As the CropSAR fAPAR values are slightly lower than the original fAPAR values, the threshold
for harvest date estimation was set at 0,36 (instead of 0,40). The results are shown in Figure
17. In a season such as 2019 with suboptimal weather conditions the correlation between the
actual and estimated harvest dates drastically increase when CropSAR fAPAR is used (R² =
0,43 compared to R² = 0,20 with original fAPAR inputs). In 2018 weather conditions were
much better. Correlations between actual and estimated harvest dates are comparable
whether original or CropSAR fAPAR inputs are used (R² = 0,71 for CropSAR vs R² = 0,78).
When combining 2018 and 2019 (in total 138 fields) correlations further increased to R²=0,99.
Figure 18 shows the error (in days) over all fields. For 44% of the fields the harvest date is
estimated with an accuracy of +/- 1 day, for 61% of the fields the accuracy amounts +/- 2 days.
Figure 17: Correlation between the harvest date for sugar beet seeds in 2018 (left) and 2019 (right) estimated from fused Sentinel-1 and Sentinel-2 images (date with cropsar fAPAR = 0,36) and the actual harvest date recorded by CAC seeds
Figure 18: Error of harvest date estimation, in days, for 2018 and 2019 (138 fields)
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Test 3: performance of the maturity model
Based on the assumption that the optimal harvest date corresponds to the date in the period
[15 June - 1 August] at which the CropSAR fAPAR value reaches the threshold of 0,36, a
“maturity model” was developed to estimate this date. The descending part (the slope) of the
CropSAR fAPAR curve was checked on a daily basis. A simple linear equation over 5 days was
used to forecast the date at which the fAPAR threshold of 0,36 would be reached.
To assess the performance of the maturity model, it was run on the full seasonal time series
of CropSAR fAPAR values (1 February – 15 August 2019) and the resulting harvest date
estimates were compared with the actual harvest dates. The results are presented in Figure
19. For both seasons (2018 and 2019) the correlations are lower than when a simple threshold
(fAPAR = 0,36) is used to estimate the harvest date (R² = 0,58 vs 0,71 for 2018 and R² = 0,19
vs 0,43 in 2019).
Figure 19: Correlation between the harvest date for sugar beet seeds in 2018 (left) and 2019 (right) estimated from fused Sentinel-1 and Sentinel-2 images (CropSAR fAPAR) on 15 August (full season) and the actual harvest date recorded by CAC seeds
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Test 4: forecasting ability of the maturity model
Finally, the forecasting ability of the maturity model was evaluated. Harvest dates were
estimated several times from the start of the harvest period end of June until the end of the
harvest period mid-August. Each time, the estimated harvest date was compared with the
actual harvest date. The resulting R² values are presented in Figure 20. Overall, the forecasting
ability of the current (linear) model is rather low.
Figure 20: Correlation (R² value) between the estimated and actual harvest dates at different times before harvest in 2018 (blue) and 2019 (green)
Results for soybeans
For soybeans the harvest date was estimated in a similar way as for sugar beets. Threshold of
fAPAR values were defined for “original fAPAR” (set at 0,23 for soybeans) and “CropSAR
fAPAR” values (at 0,18) based on the actual harvest dates of the 41 soybean fields that were
monitored in 2019. As shown in Figure 21, the correlations between the estimated and actual
harvest dates were significantly higher when CropSAR fAPAR input was used (R² = 0,49 vs 0,35
for original fAPAR input).
Figure 21: Correlation between the harvest date for sugar beet seeds in 2019 estimated from (left) original Sentinel-2 images (date with fAPAR = 0,23) and (right) fused Sentinel-1 and Sentinel-2 images (date with CropSAR fAPAR = 0,18) and the actual harvest date recorded by CAC seeds
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Figure 22 shows the error of the harvest date estimation (in days) over all fields. For 49% of
the fields the harvest date is estimated with an accuracy of +/- 2 days, for 72% of the fields
the accuracy amounts +/- 3 days.
Figure 22: Error of harvest date estimation for soybeans, in days, for 2019 (41 fields)
To estimate the harvest date of the soybeans a “maturity model” was developed following
the same (linear) approach as for sugar beets. The performance of the model was checked by
comparing the estimated harvest dates, derived from a full seasonal time series of CropSAR
fAPAR values, with the actual harvest dates recorded by CAC seeds (Figure 23). The
correlations obtained with the model (R² = 0,53) were similar to the correlations obtained
when a simple threshold (0,18) was used to estimate the harvest date (R² = 0,49).
Figure 23: Correlation between the harvest date for soybeans in 2019 estimated from fused Sentinel-1 and Sentinel-2 images (CropSAR fAPAR) on 20 October (full season) and the actual harvest date recorded by CAC seeds
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Results for sunflowers
The harvest of sunflower is conditioned by the status of the plants as the stems and the heads
need to dry perfectly before harvesting, so as the seeds will be released without damage
(Figure 24). Hence, the maturity of the seed does not correspond to the ideal stage of
combining. In contrast to sugar beets, sunflower cannot be cut and let dry in the field, so the
fAPAR index is not helpful for assessing harvesting time. Nevertheless, we took advantage of
tools developed during the project to assess the different maturity stages in relation to the
moisture of the seed.
In 2019 three sunflower fields were monitored for five weeks during maturation; samples of
the heads containing the seeds were taken from each field and brought to the CAC’s
laboratory where the seed was removed from the heads and tested for moisture and
germination. On each day of sampling the fAPAR index of each field was recorded.
The fields showed a progress in germination correlated with a reduction of moisture and
fAPAR index, as expected. The value of index at which the seed reached full germination in
the three varieties monitored was approx. 0,20, however this has been considered as a
preliminary test. Further investigation in additional fields would be desirable to set up a rule.
Figure 24: Sunflower field at harvesting stage
Sugar beets and soybeans: conclusions and possible improvements
From the pilots of sugar beets and soybeans in 2018 and 2019 it was found that, since optical
satellites are unable to look through clouds, the use of the index showing the fraction of
absorbed photosynthetically active radiation derived from Sentinel-2 images has limited
accuracy in cloudy days. If clouds persist for several days, the fieldsmen are “blind” and the
advantage of the tool fades. Introduction of fused indexes based on optical and radar data
can overcome this problem.
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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To get an optimal response from EO, however, fieldsmen have to draw the polygons
representing the fields with accuracy. The “pixel” reported by satellites of 10m x 10m can
distort the index in case ditches, side roads or fractions of neighbouring fields are included in
the polygon.
The final conclusion therefore is:
• It is possible to estimate the optimal harvest date from the fAPAR curve with a
moderate to high accuracy when using fAPAR threshold values.
• The accuracy of the harvest date estimation increases when CropSAR and fAPAR
values are used, especially in cloudy periods.
• The maturity model that is currently used to forecast the harvest date (simple linear
approach to estimate the date that the fAPAR threshold is reached) is not accurate
enough.
Components, datasets and pipelines
The pilot uses C08.02 Proba-V MEP EO component for processing, analysing and visualizing
the Sentinel-2 fAPAR data.
DataBio component deployment status
Component code
and name
Purpose for pilot Deployment status Component
location
C08.02 (Proba-V
MEP)
Sentinel-2
processing,
dashboards,
services for viewing
and time series
extraction
Adapted according to the
needs of pilot A1.2
Proba-V MEP
at VITO
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4.4.3 Data Assets
Data Type Dataset Dataset
original
source
Dataset
location
Volume
(GB)
Velocity
(GB/year)
EO data Sentinel-2
processed data
(raw data ->
fAPAR)
ESA Proba-V
MEP at
VITO
2630 GB 1850 GB
Exploitation and Evaluation of pilot results
Pilot exploitation based on results
The performance of the maturity models could be improved by using more advanced
modelling techniques such as curve fitting, or by using machine learning techniques to predict
fAPAR values. The use of meteo data (rainfall, temperature) as additional input for maturity
modelling may also improve the accuracy of the harvest date estimation.
To enhance the reliability of the model is necessary to continue with EO adding more data to
the model and checking with on-site reports the factors which can distort the parameters.
The usability of the tool also has to be further improved in terms of speed and user
friendliness; fieldsmen are often out of their office and they need to get the platform adapted
to mobile application with easy access and easy handling.
KPIs
During the stages of the project KPI could not be measured, but just estimated.
In effect the fieldsmen did not spare any travel but, on the contrary, they had to drive more
and make more reports to collect the information needed to support the project.
The advantages of having a reliable support in assessment of maturity of the seed crops can
be estimated in: Reduced number of visits to the fields close to maturity stage, Increased
efficiency in assisting the growers in harvesting operations and increased efficiency in
warehouse planning and logistics.
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KPI
short
name
KPI
descrip
tion
Goal
description
Base
value
Target
value
Measured
value
Unit
of
value
Comment
KMT Numb
er of
km
driven
by car
Reduced
km driven
100%
(actu
al
total
yearly
journ
ey)
85% Estimated
reduction
of 15% of
the km
driven by
fieldsmen
using the
tool
NOF Numb
er of
farms
contro
lled by
each
Fields
man
Increase of
the
number of
farms
controlled
100%
(actu
al
numb
er of
farms
)
120% Estimated
potential
increase
of
efficiency
due to
the tool
The outcome of the pilot confirms that satellite-driven technology in agriculture can be used
not only for assisted drive of tractors. Joining EO with IoT and sensors is the future of
agriculture.
Farmers are by nature conservative, but the development of the new technologies is going to
rapidly change the future of agriculture. The introduction of tools and devices for the control
of harvesting operations contributes in making operators aware of the importance of being
“on the spot”, ready to take advantage of the innovations that IT offers in this very traditional
sector. The dissemination of this awareness can be considered – besides the expected
performances of KPI - one of the goals of this pilot.
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5 Pilot 3 [A1.3] Precision agriculture in vegetables_2 (Potatoes)
Pilot overview
The product developed by NB Advies with the help of VITO is a system to generate ‘vigor’
maps for potato growers in the Netherlands, using Earth observation and weather data
sources combined with field information. The maps are included into an online platform for
monitoring and early warning of inhomogeneity. Yield prediction data can be made available
in an early stage of the growing season, though the accuracy is not sufficient due to the lack
of reliable training data.
Summary of pilot before Trial 2
For the Trial 1 in 2018 the Sentinel data are being systematically processed for visualisation
in the app. There is ongoing work on the improvement of the cloud coverage issues
(smoothing, data fusion) in WP5.
It was intended that daily weather updates from KNMI (Dutch weather services) would be
added for aggregated visualisation in the app. Unfortunately, this service stopped providing
data in February 2018. A group of 10 farmers were selected for the first trial, providing
detailed data about their crops, like the variety, the plant date and their mid- and end season
yield data.
Preliminary results are visualisation of fAPAR (biomass index) from Sentinel 2 EO data of the
area of interest, presenting new imagery every 5-10 days (if cloud coverage permits). The
WatchItGrow® app can be used by the farmers for data entry of parcel information, like crop
variety, plant date etc. Graphics of fAPAR development over time per parcel and compared
to similar parcels in the surrounding area are shown by the pilot (Figures 25 – 28).
Figure 25: Processed Sentinel data into Greenness; available for the growing season (A1.3)
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Figure 26: Greenness graph during growing season (A1.3)
Weather information graphics of weather data sequence, stating temperature and
precipitation were added to the interface. Data were also used in several demonstrations,
e.g. the impact of the drought in summer 2018 and the impact of irrigation (center pivot) for
mitigating the drought.
Figure 27: Image demonstrating drought in Summer 2018 from Sentinel data (A1.3)
Data were also used in a preliminary study on the impact of greenland management on the
resilience of the grassland against climatic change impacts like drought and intense rainfall.
Figure 28: Analysis of greenland management based on the greenness from Sentinel data (A1.3)
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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In Trial 1 a general service based on the WatchItGrow® web application was made available
to the farmers. From the feedback by the farmers we could conclude some suggestions:
• fAPAR data are hard to interpret and understand by the farmers. The maps were
useful for showing the inhomogeneity but were not actionable data. Maps using LAI
(Leaf area index) are better to understand by the farmers.
• Maps should give more insight in the actual situation compared to the potential of the
field and crop growth in values relative to the potential.
• Farmers are not willing to visit a website in order to find whether new EO images are
available; an alert service should warn them only when their action is required.
Preparation and execution of Trial 2
Trial 2 timeline
January - June 2019: Collecting historical data (2017-2018) for a preliminary analysis and
comparison of different crop models, preparing the gathering and processing current year’s
data in a crop growth model.
June - October 2019: Running the prototype with group of farmers, comparison of model
results and EO field data and reports for the farmer.
Preparation for Trial 2
In preparation of Trial 2 the use of the crop growth model WOFOST (WOrld FOod STudies)
was introduced. A decision support system was created using simulated potential and water
limited crop growth based on weather and soil parameters, respectively (Figure 29).
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Figure 29: Concept of a simple (starch) potato DSS
Soil, crop and weather data from field measurements, satellites, weather stations, literature
and other sources were collected and, after pre-processing and storage in a database, were
used as input in a crop growth model. The model then establishes the benchmark crop
performance: an estimation of the best possible performance under the given set of
circumstances. For the calibration, model data are compared with historical EO data.
The collected datasets include:
• Soil characteristics map BOFEK2012 spatial dataset for the Netherlands with soil
physical units, representing areas of corresponding soil structure and hydrological
behaviour (Figure 30)
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Figure 30: Map of soil characteristics for the Netherlands
• Weather data (temperature, precipitation, radiance, evapotranspiration) of different
KNMI weather stations (Figures 31, 32; example growing season average temperature
and daily sum precipitation) measured daily. For each field the nearest weather
station was selected.
Figure 31: Weather data (precipitation per day vs temperature) from weather stations
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Figure 32: Weather data (precipitation) from weather stations
• Soil moisture sensors (Figure 33, example one month); measured once per hour. In
each of the pilot fields, soil sensors (IoT) were installed to record soil moisture data.
Figure 33: Soil moisture sensors
Input data for the model were collected and transformed into the WOFOST format.
Trial 2 execution
The pilot aims to create a Big Data analysis platform for farmers based on Sentinel-2 data, as
a DSS system that will provide benchmark information of simulated potential and water
limited crop growth, in order to get a higher yield (in dry matter) at lower costs.
The study area is located in the region of Veenkolonien (ca. 51.000 ha) in Northern
Netherlands. This area is characterized by large scale arable farms. In 2007 already 37% of the
farmers were >100 ha in size and this number is growing.
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Figure 34: A1.3 general location
In the pilot stage 2, eleven (11) farmers selected one of the fields on their farm, gathering in
total 111 ha (Figure 35).
Figure 35: Farm areas selected for the pilot A1.3
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Online platform
The objective was to create an online platform for farmers for crop monitoring and
benchmarking, showing the in-field variation. Sentinel-2 satellite images are very helpful for
crop monitoring over large areas; yet for use in a DSS it is more useful to show just the field
information and not the complete images (Figure 36).
Figure 36: Online platform for crop monitoring and benchmarking
Crop growth model
The following Big Data sources were processed:
• Daily measured weather data (temperature, precipitation, radiance,
evapotranspiration) of different KNMI weather stations
• Soil characteristics map according to the BOFEK2012 classification, representing areas
of corresponding soil structure and hydrological behaviour
• Hourly measured soil moisture sensors
• Sentinel-2 with an average interval of 5 days
In order to benchmark crop performances, the WOFOST crop growth model was introduced
and was calibrated using historical (2017, 2018) and recent samples.
Processing of images refers to:
• Applying cloud mask, and cloud-shadow mask
• Calculating a-factor (nir soil / red/soil) for WDVI, based on bare soil
• WDVI=NIR - (nir bare soil/red bare soil) * RED
• Calculating WDVI from spectral data
• Calculating LAI for potato fields based on WDVI-LAI correlation data (Figure 37).
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Figure 37: LAI-WDVI polynomial regression model for spring potatoes achieving high r2. doi: 10.1117/12.2029099
UAV spectral data
In cooperation with a potato breeding farm several crop index data were gathered for the
varieties that were also planted by the farmers. The layout of the test plots is presented in
Figure 38. The trial fields were monitored by UAV (Unmanned Aerial Vehicles) once a month
(June, July and August) gathering multi spectral data (Figure 39). By processing the UAV data
multiple crop indices, including yield potential, were calculated for each plot. Different
varieties are known to have different phenological development. From the average crop index
values for each variety significant differences in crop development between the varieties
were expected; differences were observed, but they were not significant. This may be due to
the weather, which was out of the ordinary in 2019, which might have dominated the crop
development.
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Figure 38: Potato trial fields
Figure 39: UAV spectral image (Red Edge NDVI -index) image taken 25 June 2019
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Figure 40: Monitoring of trial fields during July and August
Figure 41: Performance of yield potential (mean values vs date)
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Trial 2 results
Crop monitoring
One of the issues after Trial 1 was that fAPAR data are hard to interpret and understand by
the farmers. The fAPAR maps were useful for showing the inhomogeneity. The online
platform shows the variability in LAI (Leaf Area Index). This index represents the area
intercepting the solar radiation for crop growth and thus, maps using LAI are more
understandable by the farmers.
The variability in the field indicates the area that need attention in the sense of limiting
factors, which may be soil characteristics, water, fertilizer or pests. For each of the pilot fields
the crop monitoring data were provided in the online platform, as presented in Figure 42,
expressed in LAI for June-September 2019. The farmers received an email alert when new
processed images were available.
This platform provided valuable information for farmers to inform them about:
• the in-field-variation and areas for inspection and site-specific management
• relative performance of their field compared to the surrounding fields
• relative performance of their field compared to the potential
• the need for irrigation (combined with soil moisture data) (Figure 43)
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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June 2019
LAI
July 2019
August 2019
September 2019
Figure 42: Crop monitoring expressing variability in LAI
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Figure 43: Soil moisture and LAI index data for the pilot fields
Yield prediction
In general, the water-limited growth model underestimates the yield and the potential
compared to the samples (Figures 44 – 46).
• The data available for validation of the WOFOST model proved to be quite limiting the
results.
• Only for 2 years data were available for comparison of model data and data from
Sentinel-2
• Only 1 year (2018) of field data with location information about the parcel were
available
• Weather conditions in 2018 and 2019 were quite out of the ordinary
• Yield differences between different varieties influenced the calibration results more
than anticipated
• The water limiting effect was quite significant, but soil moisture data for previous
years were not available
Due to limited data availability, the algorithm is not sufficiently trained for reliable yield
predictions.
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The potential yield prediction (dry matter) based on the weather data of the last 10 years
shows the relative differences between the years, but largely overestimates the yield at
harvest time.
Figure 44: Prediction dry matter, beginning of July 2019
Figure 45: Data for the water-limited growth model
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Figure 46: Water limited crop growth model without groundwater
Comparison of the model prediction to the actual samples taken in the fields show the same
trend for the beginning of July and for harvest-time (mid-September); an over-estimation of
the potential and under-estimation of the water limited model calculations for the pilot fields.
Both in dry matter and total yield.
Figure 47: Dry matter and total yield for pilot fields during the beginning of July and harvest time
Yield improvement
It is known that the best conditions for high yields in a field are created during spring. That is
having crop emergence at the beginning of May and full crop coverage by the 10th of June,
which should remain until the end of August. Moreover, full water supplies are essential for
retaining this curve.
In the current pilot the effect of later seeding date and subsequently later crop emergence
data were tested.
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Figure 48: Potential crop production (A1.3)
Seeding date vary from April 10th to May 8th resulting in differences in dry matter in
potatoes, ranging between 2.9 – 5.3 ton/ha on August 8th. This underlines the known rule
that yield improvement is best implemented during spring.
The upward trend of the yield prediction from the samples in July point towards the objectives
getting within reach.
Figure 49: A1.3 samples
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Components, datasets and pipelines
From the current pilot, several datasets were produced:
• Sentinel-2 images
• KNMI Weather data (solar radiation, temperature, precipitation) based on the station
closest to the field
• Multispectral drone data (for potato-variety specific vegetation index data)
• Field data from farmers (field location, planting data, potato variety, irrigation data)
• BOFEK2012, spatial dataset for the Netherlands with soil physical units, representing
areas of corresponding soil structure and hydrological behaviour
Components:
• The WOFOST crop growth model was used to determine the reference crop growth
for benchmarking the actual crop growth from the Sentinel-images with the potential
crop growth and yield prediction per field based on the actual weather data.
• For Trial 2 additional algorithms were developed to automate the search and retrieval
of Sentinel-2 images. The images are filtered on maximum cloud coverage and clipped
to the farmers’ fields to focus on relevant parts of the images. For the purpose of the
pilot additional vegetation indices NDVI, WDVI and LAI (potatoes) are calculated. In
addition, cloud masks and (experimental) cloud shadow masks are applied.
• A script was created to retrieve the weather data from the KNMI (Dutch weather
service) and transform them into a valid format for the WOFOST crop growth model
DataBio component deployment status
Component
code and
name
Purpose for pilot Deployment
status
Compone
nt location
C08.02
(Proba-V
MEP)
Sentinel-2 processing, dashboards,
services for viewing and time series
extraction
Tested during
Trial 1
Proba-V
MEP at
VITO
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Data Assets
Data
Type
Dataset Dataset
original
source
Dataset location Volu
me
(GB)
Veloci
ty
(GB/y
ear)
EO data Sentinel-2
processed
data (raw
data ->
faPAR)
ESA Proba-V MEP at VITO 2630
GB
1850
GB
Raster BOFEK2012 WUR https://www.wur.nl/nl/show/Bo
demfysische-Eenhedenkaart-
BOFEK2012.htm
<1 GB
Vector LPIS Georegister https://geodata.nationaalgeoregi
ster.nl/brpgewaspercelen
<1 GB
Vector Soil
moisture
IOT https://monitor.sensoterra.com/
login
< 1GB
Raster UAV
Spectral
data
NB Advies local <1 GB
Alpha
numeric
Weather
data
KNMI https://data.knmi.nl/datasets/ra
dar_corr_accum_24h/1.0
<1 GB
Alpha
numeric
Field data Farmer local < 1GB
Exploitation and Evaluation of pilot results
Pilot exploitation based on results
New Business opportunities can be found in:
• Implementing the yield prediction model that was tested in the pilot with AVEBE, but
also with other potato processing cooperatives.
• Implementing, with other partners in the Netherlands, the farmer decision support
system. This may be the processing cooperatives, but also other stakeholders.
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• Elaborating on the potato growth model to create new services like variable rate
application and irrigation planning.
KPIs
KPI
description
Goal description Base
value
Target
value
Measur
ed
value
Unit of
value
Comment
No of
farmers
reached in
demonstrati
ons
In order to get
farmers committed
to invest in or start
using Big Data
applications they
need to be aware of
the opportunities
for their operation.
0 250 50 Number
of
farmers
During the
pilot the
quality of the
results were
limiting the
involvement
of more
farmers.
No of
agricultural
organisation
s involved
Agricultural
organisations are
providers of services
and knowledge
transfer. They need
to be involved to
motivate farmers to
adaption.
0 4 1 Number
of
organisa
tions
Averis
No of app
builders
reached or
involved
The pilot is just the
first step in getting
Big Data
applications across
to farmers. To
spread the use of Big
Data app builders
need to be involved
to build new
applications
0 5 1 Number
of app
builders
Fieldfromspac
e.nl
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No of
proposals
for change
The basic
application needs
will be extended and
improved based on
the users’ needs.
The more proposals
for change, the
more lively the user-
community proves
to be.
0 10 n/a No fo
RFC
Not
commercially
available yet
No of
registered
farmers
The number of
registered users is
an indicator of
effectiveness and
usefulness of the
pilot
0 50 n/a No of
farmers
Not
commercially
available yet
No of
additional
use cases
The number of use
cases implemented
is an indicator of
effectiveness and
usefulness of the
pilot
0 10 3 No of
use
cases
Online
Platform
Crop
inspection
Crop
benchmarking
No of
planned
projects
Future
implementations of
the Big Data
applications could
be enhanced in
future projects.
0 2 1 No of
projects
Fieldfromspac
e.nl
No of
positive
responses
Stakeholders will be
interviewed on the
project results. The
average response
should be above
neutral to be
accounted for as a
positive response.
0 65% ? % of
respond
ents
Responses
from farmers
of pilot fields
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Starch per
ha
Realizing the 20-15-
10 goals6
13.77 15 5.6 -
11.9
tons / ha Due to
unfavourable
weather
conditions in
2019;
Upward trend
from 2013
Variable
costs per
100 kg
starch
Realizing the 20-15-
10 goals8
12,59 10 - € / 100
kg starch
Input data not
available
More
reliable
yield data
Currently the yield
predictions are
based on sampling
in July and
September.
Increasing the
accurateness of the
prediction based on
the Big Data
implementation will
be a benefit for the
sales team.
< 5% < 4% n/a %
deviatio
n from
total
realised
yield
Due to limited
data
availability the
method could
be tested, but
the algorithm
is not
sufficiently
trained
Starch
content
The starch content
of the potatoes is an
indicator for the
quality. Although
the starch content
may vary from
potato varieties, the
average starch
content should be
around 20%
? 20% 20.1% % starch
content
20.1% at
harvest-time;
21.4% at 1st of
September
6 Avebe project 20-15-10; goals set for 2020. 7 Reference: average value 13,7 tons in 2012. 8 Avebe project 20-15-10; goals set for 2020. 9 Reference: average cost €12 - €13 in 2012.
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Overall, the target of farmers is always the improvement of their yield and/or reduction of
cost. In this pilot we focused on the yield (both yield prediction and yield improvement),
because field data about the inputs were not available.
When application of fertilizers and pesticides are becoming more time- and site specific
according to the crop monitoring data, the inputs will decrease in the future.
Expected trends:
Higher harvested quantity / Fertilizer
consumption
Over a longer period, this is the trend, but
during the pilot period this could not be
demonstrated
Higher harvested quantity / Pesticide
consumption
In potatoes, the pesticide use is
predominated by the (un)favourable
weather conditions for Phytophthora. A
higher yield may come with a higher crop
protection due to more rain, which is
favourable for crop growth, but also for
Phytophthora
Higher harvested quantity / Irrigation
water quantity
The irrigated area has increased, resulting
in a higher yield of approx. 5 ton dry
matter/ha, depending on the soil, irrigation
intensity etc. A trend to a better irrigation
efficiency is not known.
Higher harvested quantity / land sq mt Over a longer period, this is the trend, but
during the pilot period this could not be
demonstrated
Higher employee productivity (Revenues /
Employee)
Higher productivity is expected, but not
demonstrated yet
Higher revenues This the objective of Big Data Analysis. In
the short term of the pilot and
unfavourable weather conditions, this
could not be demonstrated.
ROI ROI on Big Data Analysis including data
collection and processing is hard to
demonstrate because the lack of
convincing data of higher yields
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Lower quality deviations The trend is that the lower quality is
disappearing and thus deviations are
getting smaller. Due to DSS this will
strengthen
Higher data usage Data usages is rising. More data is collected
from harvest machines, UAV, satellites and
sensors. Farmers take more data-driven
decisions and apply site-specific
management.
Higher data quality Data quality is becoming more an issue,
now more data is available. Cross
referencing different data sources provide
more insight about the good/bad quality of
the data. This will lead the way to better
data quality
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6 Pilot 4 [A2.1] Big data management in greenhouse eco-system
Pilot overview
The pilot A2.1 was designed to implement Genomics Prediction Models (Genomic Selection -
GS) as a solution to technological limitations met with current breeding approaches. Indeed,
phenotypic selection (PS) and marker-assisted selection (MAS) breeding strategies represent
modern approaches upon which world agriculture have relied upon heavily. Although PS
allowed early green revolution in the mid-twentieth century, it is by now recognized that its
contribution has reached a plateau. On the other hand, thousands of marker-trait
associations uncovered in the MAS process have not been routinely exploited mainly due to
intrinsic limitations of this technology. It is out of this context that this pilot A2.1 was
designed. The pilot was run by a collaborative effort between CREA (Italy) and CERTH
(Greece). GS is a new paradigm in agriculture and demonstrates superior results in relation to
other approaches implemented thus far. Different assumptions of the distribution of marker
effects are accommodated, in order to account for different models of genetic variation
including, but not limited to: (1) the infinitesimal model, (2) finite loci model, (3) algorithms
extending Fisher’s infinitesimal model of genetic variation to account for non-additive genetic
effects. Many problems are modelled including the performance of new and unphenotyped
lines, untested environments, single-trait, multi-trait, single-environment, and multi-
environment. Genomic selection allows integrating quantitative genetics and population
genetics in a novel GS breeding approach wherein intercrosses are driven by genomic
predictions. Models are fed several data types: open-field phenotypic data, biochemical data,
phenomic and genomic data. Subsequently, these equations are used to predict the breeding
values of genotyped but unphenotyped candidates. In the process, several other Big Data
types (e.g., those describing environmental properties) can be used as covariates. The
Genomic Selection technology is expected to significantly improve genetic gain by unit of time
and cost, allowing farmers to grow a better variety sooner relative conventional approaches,
making more income. Specifically, for this pilot, the production of tomato Big Data from the
Greenhouses was slower than anticipated due the need of the production of new genetic
data, in order to assess the genetic variability of the crosses and the collection of
environmental and phenotypic data. However, preliminary results can be derived from the
application of the GS model on the genomic data since an extensive diversity study was
carried out with ddRadseq technology. Despite this, it was not possible to validate the C22.03
on tomato ddRASeq genomic data in combination with the phenomic data. As there were a
suitable amount of genomic and phenomic data from biomass sorghum pilots, in the last year
of the project, the potential of GS algorithms was successfully assessed in sorghum crops to
improve health-promoting compounds used to manufacture specialty foods. The same
approach is aimed to be tested on the tomato data once the collection of metabolic data is
complete.
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Summary of pilot before Trial 2
The first stage of the trials started in 2018. In this year, the CREA’s platform for Genomic
prediction and selection was detailed to accommodate CERTH’s requirements following a
non-conventional approach. For this purpose, CERTH initiated a pilot study for the
identification of best tomato crosses bearing desirable traits e.g. organoleptic, nutritional
value, tolerance on various environmental conditions. The parental lines are Greek varieties
that are well adapted to the local environmental conditions. In order to investigate as many
crosses as possible, an holistic approach was applied for the best evaluation of the new
genotypes, including: (1) biochemical characterization and nutritional value assessment
(2)next generation sequencing protocols to generate genomic/genotypic datasets; (3)
environmental indoor data: air temperature, air relative humidity, solar radiation, (4)
environmental outdoor data: wind speed and direction, evaporation, rain; (5) farm data: farm
logs (work calendar, technical practices at farm level, irrigation information); farm profile
(static farm information, such as size, crop type, etc.). Biochemical, genomic and phenomic
data were collected in tomato (landraces and several recombinants lines in diverse filial
progeny stages) raised in glasshouses (Figure 50).
Figure 50: Tomato accessions in glasshouse under breeding settings
CERTH also produced an initial molecular dataset through NGS (Next Generation Sequencing)
technology based on Double Digest RADseq approach (Figure 51) and performed the initial
analysis and validation based on the STACKS pipeline (Figure 52). One hundred and thirty-
eight samples, originating from 40 tomato lines were included for the study and whole-
genome genotyped using the ddRADseq protocol. Analysis with STACKS pipeline resulted in
39,618 SNPs (Single Nucleotide Polymorphisms), using the Solanum lycopersicum as reference
genome (assembly SL3.0). After quality control and removal of SNPs that did not meet the
pre-specified thresholds, 10,402 SNPs remained to be further evaluated. In total, after next
generation sequencing (NGS) 3TB raw data, including the scanned images, were produced for
further implementation in GS algorithms. The size of the SNP marker matrix was enough to
start running the model, but the number of genotyped individuals was still low to be usefully
used to run the predictive models. More data, particularly increasing the size of tomato
population phenotyped and genotyped with whole-genome marker (SNPs) information was
needed and expected in the third year (2019) of the project.
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Figure 51: ddRAD protocol modified from Peterson et al., 2012. PMCID: PMC3365034, DOI:10.1371/journal.pone.0037135
Figure 52: The STACKS pipeline, available at http://catchenlab.life.illinois.edu/stacks/manual-v1/
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In the meantime, CREA set up and anticipated a GS platform for accommodating the
upcoming genomic and phenomic/phenotypic data from CERTH’s tomato breeding. In
addition, CREA set up a genotyping and phenotyping platform integrated in sorghum pilots
(B1.3) for use as testbed of the CREA’s C22.03 (Genomic models) component (Figure 53).
Figure 53: CREA’s sorghum pilot fields used in the C22.03 genomic models platform
The DataBio algorithms were implemented as DataBio C22.03 component which is registered
under DataBio platform (https://www.databiohub.eu/registry/#services?tag=C22.03) and is
deployed by CREA. To achieve the predictive analytics run in 2018, available public datasets
were used, and the outcome was encouraging. The analytics anticipated a single and several
environments to mimic single or several glasshouses. In a single environment, we
implemented standard genomic modeling predicting performance of unphenotyped plant
materials. On the other hand, experiments were run under multiple environments scenarios.
CV1 reflected prediction of tomato lines that have not been evaluated in any glasshouse trials.
CV2 reflected prediction of tomato lines that have been evaluated in some but NOT all target
environments (glasshouses). The rationale being that prediction of non-field evaluated lines
benefits from borrowing information from lines that were evaluated in other environments
(glasshouses). This is critical in cutting costs for varietal adaptability trials of large number of
lines in several target environments.
BRR (Bayesian Ridge Regression), GBLUP (Genomic Best Linear Unbiased Prediction), LASSO
(Least Absolute Shrinkage and Selection Operator), and Bayes B were implemented in this
first trial. Under several environments, these algorithms were combined with environments
to generate further predictive analytics. For each algorithm, predictive analytics were run on
a single environment basis, across environments, marker x environment, and the approach
reaction norm model. In this report, the computational power of multiple environments and
reaction norms was illustrated using GBLUP algorithm. Our findings for the 2018 trial showed
that genomic models perform equally under single environments. On the other hand, under
multiple environments, CV2 was superior to CV1. Under CV2 settings, single-environment
model performed poorly. The equal marker effects across glasshouses worked well in relation
to the single-environment model. Accounting for marker information x environment or
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implementing the reaction norm model performed comparably and produced superior
results.
Preparation and execution of Trial 2
Trial 2 timeline
The production of tomato Big Data from the Greenhouses was slower than anticipated, since
only two generations could be produced within a year, and moreover, development of large-
fruited tomato cultivars was slower than expected. Hence, it was not possible to validate the
C22.03 on tomato related data. Yet, assessment of genetic variability of the parental lines and
their crosses was conducted, using different algorithms. In addition, biochemical parameters
of both parental tomato lines and their crosses were measured and an association with
genetic variability was investigated.
January - November 2019: Greenhouse preparation and collecting phenotypic data,
metabolomic data, IoT data from tomato cropping greenhouses. Tomato sampling, ddRAD
library construction, next generation sequencing and bioinformatics analysis of NGS data.
Biochemical and nutritional characterization of tomato cultivars. Monitoring of phenotypic
data of F6 and F7 generations.
January - April 2019: seed calibration for annual sorghum genotypes, processing data from
preliminary sorghum trials (2017) and first year (2018) trial.
April - October 2019: Sorghum trials establishment, phenomic and genomic data collection,
pilot data integration and processing, preparation of reports and writing peer-reviewed
papers.
Preparation for Trial 2
To prepare the Trial 2 stage, experimental protocols were designed, glasshouses seedbeds
were set up, open-fields sites identified and prepared according to regional recommendation,
particularly in terms of fertilization and phytosanitary measures. Seeds were calibrated in
time in order to anticipate the right planting density. For each tomato cross, ten seeds were
seeded in greenhouses, to ensure that at least three individual plants will be developed and
sampled to further study their genetic, phenotypic and biochemical properties.
Trial 2 execution
Trials were sown on time and managed according the designed experimental protocols.
Greenhouses for tomato pilot trials were established in Greece, whereas sorghum pilot trials
were established in Bologna, Italy. Tomato lines were genotyped using the double digest
restriction-site associated DNA (ddRADseq) approach, while sorghums were genotyped using
a genotyping-by-sequencing (GBS) strategy.
Concerning tomato samples, sixty-nine samples were analysed in addition to the ones
produced in 2018, resulting in 207 samples originating from 9 parental lines, clustering in 51
different populations. Crossings of nine (9) parental lines were followed and analysed, up to
generation F7. DNA was extracted from young leaves using the NucleoSpin Plant II, Macherey-
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Nagel kit. DNA concentration was evaluated on a Qubit 4.0 fluorimeter, using the Qubit
double-stranded DNA BR assay kit (Life Technologies, Carlsbad, CA) and its integrity was
assessed on a 0.8% TAE agarose gel. Two-hundred and seven NGS libraries were constructed
by applying the ddRADseq protocol (Figure 51), using the EcoR1 and MspI as restriction
enzymes. All libraries were quantified with fluorometric quantification using the Qubit®
dsDNA BR assay kit and their molarity was calculated in relation to their size after indexing.
Quantitative PCR (qPCR) was conducted on a Rotor-Gene Q thermocycler (Qiagen) with the
KAPA Library Quantification kit for Illumina sequencing platforms (KAPA BIOSYSTEMS).
Next generation sequencing was performed at the Institute of Applied Biosciences of the
Centre for Research and Technology Hellas, on an Illumina NextSeq500 platform (Illumina
Inc., San. Diego, CA, USA) using the NextSeq™ 500/550 High Output Kit (2 x 150 cycles).
Overall, 572.480.546 sequences of 150 bp (171 Gb) were produced to be annotated in the S.
lycopersicum reference genome. NGS data were analysed using the reference-based STACKS
v2.41 pipeline (Figure 52). Analysis was performed on the in house HPC Cluster at INAB
allocating 88 cores and 512 Gb RAM to analyse ddRadseq results. Results were analysed by
applying different thresholds for inclusion/exclusion of SNPs and individual plants. Further
filtering was conducted in PLINK v1.90 to reduce biases and incorrect inferences due to
missing data (both for individuals and SNPs) and by Minor Allele Frequency (MAF). Post-
filtering in plink was made based on the guidelines available at
https://rdtarvin.github.io/RADseq_Quito_2017/main/2017/08/03/afternoon-ddrad-
stacks.html. Plink filtering was conducted for basic summary statistics by applying options for
missing rate per SNP (--geno), missing rate per person (--mind) and allele frequency (--maf).
Again, loose and stringent criteria were used for the inclusion/exclusion of SNPs and
individuals.
Biochemical analysis and nutritional value assessment was carried out in the initial parental
lines and on the final genotypes as to evaluate the breeding process. For this purpose, a
thorough biochemical analysis was carried out implementing both colorimetric and
chromatographic methods. Total sugars and soluble solids were measured with a
refractometer and expressed as Brix values, total polyphenol content was measured with
Folin-Chiocalteu method, total antioxidant activity was assessed with DPPH radical assay,
lycopene was measured spectrophotometrically, total flavonoid content was measured with
AlCl3 method, ascorbic acid was assessed with Megazymes ascorbic acid assay kit and amino
acids was measured with GC-MS with EZFaastTM Free (Physiological) Amino Acid Analysis kit
(Phenomenex).
The phenotypic characterization of F6 and F7 crosses was carried out according to the UPOV
guidelines. In Table 4, the morphological characteristics of different parts of the plant are
presented.
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Table 4: Morphological traits of the plant, flower and leaf in 14 tomato genotypes according
to the UPOV guidelines.
Genotype Type of growth
Anthocyanin in the
upper 1/3 of the stem
Flower
color
Fasciation of
the 1st flower
Leaf attitude at the
middle 1/3 of the plant
Type of
leaf blade
Attitude of petiole of leaflet in
relation to main axis
F6 11x1_a indeterminate weak yellow absence
semi-drooping bipinnate horizontal
F6 11x1_b indeterminate absent or very weak yellow absence
semi-drooping bipinnate horizontal
F6 3x1_f indeterminate absent or very weak yellow absence horizontal bipinnate semi-erect
F6 3x1_e indeterminate absent or very weak yellow absence horizontal bipinnate semi-erect
F6 3x1_d indeterminate absent or very weak yellow presence drooping bipinnate horizontal
F6 3x1_c indeterminate absent or very weak yellow presence
semi-drooping bipinnate horizontal
F6 3x1_a indeterminate absent or very weak yellow presence drooping bipinnate horizontal
F6 3x1_b indeterminate weak yellow presence
semi-drooping bipinnate horizontal
F6 1x9 indeterminate absent or very weak yellow absence drooping bipinnate semi-erect
F7 32x30 indeterminate absent or very weak yellow absence semi-erect bipinnate semi-erect
F7 17x32_b indeterminate weak yellow presence
semi-drooping bipinnate semi-erect
F7 17x32_a indeterminate weak yellow presence
semi-drooping bipinnate semi-erect
F7 32x36_a indeterminate absent or very weak yellow absence drooping bipinnate horizontal
F7 32x36_b indeterminate absent or very weak yellow absence
semi-drooping bipinnate horizontal
As it is presented in the table, most of the morphological characteristics are alike among the
different genotypes. Two characteristics had the highest variability, leaf attitude at the middle
1/3 of the plant and the attitude of petiole of leaflet in relation to main axis. Since the climate
of Greece is characterized by high temperatures during summer, the ability of the plant to
tolerate heat stress was validated. As it is demonstrated in Table 5 a significant variability was
observed among the tomato genotypes. The most tolerant crosses were F6 11x1_a and
F6_3x1_e and F6_3x1_d.
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Table 5: Plant vigor and tolerance to high temperatures in 14 tomato genotypes.
Genotype Plant vigor1 Tolerance to high temperatures1,2
weak
(%)
medium
(%)
good
(%)
very good
(%)
low
(%)
medium
(%)
high
(%)
F6 11x1_a 19 81 46 42 12
F6 11x1_b 3 54 43 84 10 6
F6 3x1_f 4 28 40 28 76 24
F6 3x1_e 5 13 21 61 80 20
F6 3x1_d 4 4 32 60 60 36 4
F6 3x1_c 5 44 51 37 46 17
F6 3x1_a 19 77 4 54 42 4
F6 3x1_b 7 17 73 3 76 24
F6 1x9 33 67 53 43 4
F7 32x30 13 33 54 34 66
F7 17x32_b 26 46 18 10 53 47
F7 17x32_a 5 40 55 30 45 25
F7 32x36_a 19 73 8 65 27 8
F7 32x36_b 10 90 34 66 1visually estimated at the end of the experiment (approximately 120 days after transplanting) 2estimated on the basis of aborted flowers during high temperatures
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Table 6:Total production traits in 14 tomato genotypes (sum of six weekly harvests).
Genotype
Production
weight (g)
Number of
fruits
Mean fruit
weight (g)
Number of fruits
with catface
Number of fruits
with cracking
Number of fruits
with B.E.R.1
Number of “off-type”
fruits2
Number of fruits
with “nose”3
Mean S.E. Mean S.E. Mean S.E. Mean S.E. Mean S.E. Mean S.E. Mean S.E. Mean S.E.
F6 11x1_a 1434,69 108,26 19,23 0,89 75,99 5,33 0,00 0,00 3,35 0,34 0,00 0,00 5,12 0,35 0,15 0,15
F6 11x1_b 1712,32 98,81 19,54 1,04 92,55 6,86 0,04 0,04 4,57 0,54 0,00 0,00 5,43 0,44 0,00 0,00
F6 3x1_f 2174,59 138,19 51,89 3,29 42,22 1,01 0,22 0,15 6,78 1,03 0,04 0,04 7,85 0,73 0,04 0,04
F6 3x1_e 1984,54 120,67 58,62 3,70 34,09 0,79 0,08 0,08 1,73 0,40 0,04 0,04 5,46 0,59 0,08 0,05
F6 3x1_d 3004,52 190,57 40,89 3,55 80,97 5,67 12,93 1,84 19,74 1,84 0,22 0,12 9,82 0,79 0,15 0,07
F6 3x1_c 3646,69 138,99 46,46 2,63 83,09 4,77 8,39 1,68 12,81 1,49 0,27 0,10 6,69 0,43 0,04 0,04
F6 3x1_a 2902,92 122,40 16,27 0,80 184,34 7,77 2,54 0,26 9,96 0,56 0,04 0,04 1,92 0,29 0,12 0,06
F6 3x1_b 2823,00 149,25 15,59 0,78 181,02 4,46 2,17 0,28 9,83 0,60 0,00 0,00 1,45 0,17 0,00 0,00
F6 1x9 2271,83 145,76 26,83 11,44 144,90 7,19 0,38 0,14 10,90 0,64 0,38 0,09 3,17 0,30 0,03 0,03
F7 32x30 3315,39 89,45 126,46 4,18 26,50 0,57 0,00 0,00 0,00 0,00 0,00 0,00 6,73 0,74 0,04 0,04
F7 17x32_b 3709,44 277,02 35,84 2,50 103,62 3,39 17,60 2,05 14,12 1,39 0,28 0,20 7,16 0,69 0,12 0,07
F7 17x32_a 3496,41 258,97 34,96 2,50 100,39 3,66 12,00 1,20 10,73 1,46 0,00 0,00 7,64 0,75 0,09 0,06
F7 32x36_a 1954,19 88,29 19,23 0,97 104,16 3,54 8,92 0,78 16,77 1,00 0,00 0,00 5,42 0,22 0,31 0,09
F7 32x36_b 2169,75 107,43 21,43 0,84 101,10 3,19 8,50 0,69 17,96 0,82 0,14 0,11 5,21 0,47 0,43 0,11
1B.E.R. = blossom end rot
2marketable fruits that had slightly different attributes (shape) from the rest
3one carpel was not properly fused with the rest of the fruit and was protruding upwards
The mean values and their respective standard errors (S.E.) were calculated from 25-28 independent measurements per genotype.
Finally, the tomato genotypes were also phenotyped regarding specific production traits. As
it is displayed in Table 6, the most productive genotypes were F3 3x1a-d, F7 32x30 and F7
17x32 a,b. The genotype F7 32x30 was also the most productive of all regarding the total
number of produced fruits.
The sorghum experimental sites for this pilot coincided with the experimental sites for the
pilot B1.3. DNA was isolated from plantlets using the GeneJET Plant Genomic DNA Purification
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Kit. DNA concentration and purity were evaluated by a Tecan Infinite M200Pro
spectrophotometer, while DNA integrity was evaluated through 1% agarose gel
electrophoresis containing GelRed (Biotium) as fluorescent dye. For each DNA sample, an
aliquot of 60 µl at a concentration ≥ 10 ng/µl was used for downstream analyses. In sorghum
the methylation sensitive restriction enzyme ApeKI was used for library preparation, and GBS
was carried out on an Illumina HiSeq X Ten platform. The sequencing reads were aligned to
the sorghum reference genome (Sorghum_bicolor NCBIv3) to enable variants discovery. The
two batches yielded two respective matrices of 933,020 and 919,485 SNP loci, and were
delivered as separate VCF files which were subsequently merged into a single matrix using
VCFtools resulting in a total of 1,252,091 loci. Marker quality control criteria were then
applied to the merged dataset considering only samples having phenotypic and marker data.
The final working matrix consisting of 61,976 high-quality SNPs was used in this work for
genomic selection and prediction analytics.
Trial 2 results
In addition to genomic data, phenotypic data were produced but the bottleneck was the
lower number of phenotyped individuals that did not allow the implementation of genomic
selection and prediction analytics. Nevertheless, several population statistics models were
applied to the dataset (Principal Component Analysis-PCA, ADMIXTURE analysis), so as to
profile the genetic background of tomato cultivars, in relation to biochemical properties of F6
- F7 plants (stable offspring), which was the aim of the current pilot. Analysis of the genetic
diversity of the 207 genotypes revealed three major clusters; one enclosing the vast majority
of the genotyped samples, a second enclosing F6 and F7 of the 32x36 cross and a third cluster
consisting of F5, F6 and F7 17x32 crosses (Figures 54 and 55; PCA analysis per population and
per individual). Notably, all replicates of the F5_3x1 cross presented exactly the same
clustering profile, indicating that the variance is low. The most diverse cross was 3x1, which
presented a loose clustering, indicative of a less stable offspring over the generations,
compared to the other crosses used in this pilot. The first two principal components (PC1 and
PC2) explained 48.87% of the total genetic variation. Admixture analysis confirmed PCA
results; the lowest cross-validation (CV) error for the 51 populations was acquired for K = 50.
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Figure 54: Principal component analysis for the tomato populations based on their genetic background
Figure 55: Principal component analysis for the tomato individuals based on their genetic background
Along with the genetic diversity of the tomato genotypes, variability of the crosses was also
assessed based on their biochemical background. For this purpose, a PCA of tomato cultivars
was conducted based on the following biochemical parameters: total sugars and solids as
expressed by Brix scale, total phenol and flavonoid content, antioxidant activity, ascorbic acid
content, amino acid content and lycopene content. Analysis of the diversity of cultivars based
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on their biochemical background showed three loose clusters of F6 and F7 crosses. A loose
clustering of F6_3x1 cross was also present in the PCA analysis of the cultivars based on the
biochemical data similar to the findings of the genomic analysis. The genetic diversity of the
crosses F6_17x32 and F6_32x36 were also depicted on their biochemical background since
did not group with any other cultivar.
Figure 56: Principal component analysis for the tomato individuals based on their biochemical background
In the open-field sorghum trials, the purpose was to assess the performance of four GS
models (GBLUP, BRR, Bayesian LASSO, and BayesB) in four sorghum grain antioxidant plant
characteristics (phenols, flavonoids, total antioxidant capacity, and condensed tannins), using
whole-genome SNP markers. One key breeding problem modelled was predicting the
performance in antioxidant production of new and unphenotyped sorghum genotypes
(validation set). The populations were weakly structured (analysis of molecular variance,
AMOVA R square = 9%), demonstrated a significant genetic diversity, and expressed
antioxidant traits with a good level of variability and highly correlated. The perennial
populations (S. bicolor × S. halepense) outperformed the annual populations (Sorghum
bicolor) for all the antioxidants. The four genomic selection models implemented in this pilot
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performed comparably across traits, with accuracy ranging from 0.50 to 0.60 (Figure 57), and
are considered high enough to sustain sorghum breeding for antioxidants production and
allow important genetic gains per unit of time and cost. The results produced in this pilot are
expected to contribute to genomic selection implementation and genetic improvement of
sorghum grain antioxidants for different purposes including the manufacture of health-
promoting and specialty foods in Europe in particular, and in the world in general.
Figure 57: Distribution (boxplot) of GS models validated accuracy in external sample (not used during model training) of 34 (30% of the total population) sorghum lines. FEN, FLA, TAC, TAN, respectively, polyphenols, flavonoids, total antioxidant capacity, and condensed tannins. Traits means are included within the boxplot. Trait means with same letter are not significantly different at the 5% level using the Tukey's HSD (honestly significant difference) test. Refer to text for the description of the GS models.
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Components, datasets and pipelines
DataBio component deployment status
Component code
and name
Purpose for pilot Deployment status Component
location
C22.03 Genomic
models
Implementing
genomic selection
analytics to
calibrate the
phenomics against
the genomics to
successively
predict the
performance of
unphenotyped
plant lines and
untested
environments,
with massive time
and cost cutting,
and meaningful
genetic gain.
Validated with real pilot
data
CREA
(ephrem.hab
yarimana@cr
ea.gov.it)
Data Assets
Data Type Dataset Dataset original
source
Datase
t
locatio
n
Volum
e (GB)
Velocity
(GB/year)
Penomics,
metabolomic
s, genomics,
environment
al data
DS-40.01 CERTH, CREA CERTH,
CREA
5000 2500
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Exploitation and Evaluation of pilot results
Pilot exploitation based on results
The phenotyping work in tomato glasshouses proceeded slower than anticipated, which did
not allow us to validate the GS algorithms in tomato materials. The algorithms were validated
only in sorghum (annual and perennial) pilots. Nonetheless, the ddRADSeq genotyping
platform was validated and can be used for sequencing and genotyping (variants calling)
services of several plant and animal breeding schemes. Current empirical evidence for
genomic selection efficiency in plant breeding set to 0.5, the baseline for genomic selection
prediction accuracy in plant breeding. In addition, recent research works demonstrated that
genomic selection accuracy as low as 0.2 can allow substantial within-generation yield
improvement. Therefore, the genomic selection model performances obtained in our pilots
are high enough to sustain sorghum breeding for antioxidants production and allow
important genetic gains per unit of time and cost. In addition to the accuracy, the importance
of the genomic selection strategy is also evaluated using other criteria, such as the possibility
that this technology offers the potential to shorten the breeding cycle, with interesting
economic returns, due to intercrosses driven by genetic predictions. Hence, in the case of
antioxidants, genomic selection offers the possibility to select for or against this trait early
(e.g., at the seed or seedling stages) without waiting for seed setting or harvest. The genomic
selection equations developed in this work can be directly used in sorghum breeding
programs. The genomic selection results presented herein and experimental designs used in
this work can be implemented in antioxidants genetic investigations and in breeding
programs to qualitatively and quantitatively improve the antioxidant production for different
purposes including the manufacture of health-promoting and specialty foods.
KPIs
KPI
short
nam
e
KPI
descripti
on
Goal
description
Base
value
Target
value
Measure
d value
Unit
of
value
Comment
A2.1-
KPI-
01
Accuracy Increased
accuracy
0.4 0.4-
0.7
0.5-0.6 Pears
on’r
Pilot was
successful
A2.1-
KPI-
02
Breeding
cycle
(years)
Decrease the
cycle relative
to
phenotypic
breeding
- 0.30 0.25 Ratio Too early
to assess
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A2.1-
KPI-
03
Breeding
costs
(index)
Decrease
costs relative
to
phenotypic
breeding
- 0.50 0.20 Ratio Too early
to assess
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7 Pilot 5 [B1.1] Cereals and biomass crop Pilot overview
The product developed by TRAGSA Group with the help of ATOS and IBM Israel is a system to
generate accurate "irrigation maps" and "vigor maps" of crops, using Big Data Sources as EO
data and sensors data as inputs. Those maps, from different areas in Spain as Castile and
Andalusia, are included in an informative management system for early warning of
inhomogeneity.
As a brief summary, this new service provides analytical and accurate data on crop
heterogeneity: due to irregular irrigation, mechanical problems affecting irrigation systems,
incorrect distribution of fertilizers or any other sources of inhomogeneity could appear crops
growing differences. Therefore, this DataBio Service is an excellent preventive tool for
farmers and landowners in order to avoid production losses and it is a powerful tool for
agricultural management in big productive areas.
Summary of pilot before Trial 2
Once the use case was defined, a first description of the required input data was decided.
Massive and rapidly updated data, bioagronomic data, sensor data, terrestrial observation
data and geographic data from different sources were used, specifically:
• SENTINEL-2: are terrestrial observation data owned by the ESA (European Space
Agency).
• Ortophotos: terrestrial observation data in image format obtained from the National
Geographic Institute of Spain.
• RPAS: are terrestrial observation data obtained by thermal and multispectral sensors,
owned by TRAGSA.
• SigPAC: Spatial data in the ESRI Shapefile format which identify the parcels, owned by
Junta de Castilla y León.
• Alphanumeric data from surveys and field visits, owned by TRAGSA.
Regarding Big Data processing, the used remote sensing data such as Sentinel-2B have an
average TB size per year, the Spanish LPIS system has a size of hundreds of GBs, in the same
way as the Spanish orthophotos project (PNOA). In terms of speed, Sentinel-2 has the highest
update rate, within the information sources considered, this being five days. All external
sources have an annual update rate. The variety of formats will include images and terrestrial
models and the variability of the agricultural information, typically depending on the seasons
of the year.
Some research has been carried out on the acquisition of own data through sensors or IoT
devices, but this sup-pilot is still in an early stage of development.
After the capture and initial collection of data, they have been stored in Mongo DB Databases
and the tasks of processing Big Data with R for the creation of models of inhomogeneities
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begun. The output of the processing of this data are output raster images (images formed by
an array of cells (or pixels) organized in rows and columns in which each cell contains a value
representing a value of a given information) and spatial databases.
As a first step, the assessment of the required satellite images and cloud processing service
platforms, part of DataBio platform, was carried out. This evaluation was made through its
application to the development of irrigation needs algorithms, in order to obtain full
functionality in web applications based on high frequency, scalable satellite image data at
national level.
Preparation and execution of Trial 2
Trial 2 timeline
Trial 1 timeline (Figure 58):
• First iteration of data acquisition + field work carried out on time.
• RPAS and field data acquisition and processing have required a bigger effort than
expected. General monitoring based on satellite images has consequently decided to
be improved in 2019, although some images are already available and pre-processed.
• Data processing periods have taken place after each field campaign, and are still on-
going
• The first implementation of these services as part of Databio platform was expected
to be performed in M18-M28 and, currently, there is a first version not including GIS
capacities yet. Although this is a bit delayed, we are working on it and expect to have
it ready for M28.
• KPIs were proposed to be measured in M12 (baseline) and M35 (final). In M12 it was
still too early to evaluate KPIs, so it was postponed, and an evaluation is included in
this document.
Trial 2 timeline (Figure 58):
• Second iteration of data acquisition + field work carried out on time.
• IoT Sensors installed. Use of ATOS Fiware Broker and PROTON.
• Final products and tools
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Figure 58: Pilot B1.1 timeline
Preparation for Trial 2
To prepare the Trial 2 stage, new data were collected and prepared according to the
specifications defined in Trial 1. In addition, the agreement with the second pilot zone Genil
Cabra, Andalusia was defined.
Trial 2 execution
The final product developed during the Trial 2 is composed by several technological elements
aimed to get the following objectives: (i) reduction of inputs as water, manure, fertilizers, (ii)
reduction in energy consumption, (iii) automation of irrigation systems, (iv) optimization of
irrigation areas management, (v) deploy of Big Data in agricultural environment, (vi)
modernize agriculture and (vii) traceability control.
The pilot objective is to integrate satellite and RPAS Big Data in decision-making support tools
in order to improve water efficiency and agriculture management for irrigated crops. The
study area comprehends more than 100 ha, nevertheless the methodology developed can be
extended to bigger areas.
Consequently, the goal accomplished was to design, use and deploy tools and processes to
create real Smart Agriculture in irrigation areas and to establish useful processes useful in
other agri-food chains.
In order to get the previous objectives, the following Big Data Sources have been processed
and used:
Internet of Things:
Agro-climatic stations provide temperature, relative humidity, absolute humidity and wind
data from the following sensors:
1. Contact sensors
2. Humidity sensors on cropped soil to know its actual conditions in order to determine
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and control the field capacity
3. Lysimeter (to control the level of nutrients in the field, adjusting the amount of manure and fertilizer needed)
4. Control in the parcels with sprinklers, drippers, metering devices…
5. Irrigation networks controls (pressure switches, pressure reducer, flow meters, manometers, Solenoid valves for an automatic opening and closure, counters, pumping stations controls: manometers, flow meters, pumping state, anti-return valves, alarm settings, heating….
Images:
The pilot uses Remote Sensing data and RPAS/UAVs generated imageries. The use of satellite
technology (SENTINEL, LANDSAT, etc…) helps stakeholders to control the general conditions
of the crop, obtaining its specific Kc10 and detecting plagues, diseases, transpiration or
excessive humidity problems. The use of this technology defines the accurate amount of
water and fertilization that the crop needs.
A comprehensive strategy combining Big Data remote sensing and field data has been helpful
for an improved and more efficient agriculture management. Satellite data are suitable for
monitoring large areas over time, while Remotely Piloted Aircraft Systems (RPAS) provide
specific data for calibration and validation purposes. Tragsa Group counts on different
solutions based on RPAS in order to fulfil these tasks.
Therefore, the results of the pilot have highlighted that despite of irrigation needed by crops
is usually calculated using Kc reference values provided by FAO, the Normalized Difference
Vegetation Index (NDVI) obtained by means of remote sensing has proven to be more useful
for calculating the Kc factor adjusted to local conditions (Figure 59).
Kc = 1.25 x NDVI + 0.1 (Calera et al, 2014)
NDVI = (NIR – R) / (NIR + R), where R means red band and NIR means Near Infrared band
Figure 59: Kc and NDVI equations
DataBio pilot B1.1 has proved that Kc values obtained by using NDVI derived from RPAS
multispectral images improved the ones provided by FAO model, and showed a very reliable
relationship with Kc derived from SPOT 7 satellite images. In addition, some products were
obtained from RPAS data, including RGB mosaics (3 cm GSD) thermal images and Digital
Terrain and Surface Models (DTM, DSM) (Figure 60). These products provide valuable
information for different purposes such as the monitoring of plants health or the estimation
of growth and biomass.
10 http://www.fao.org/3/X0490E/x0490e0b.htm
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Figure 60: Left to right: NDVI image from multispectral RPAS data; RGB mosaic; thermal image over RGB mosaic; DSM
Finally, Big Data have provided new efficient decision-making tools for helping agricultural
development as well as biodiversity protection. New acquired, aggregated and shared data
can serve as a breeding ground for extracting and sharing useful information and knowledge
among different actors, as well as for combining large data sources (especially regarding
weather models and earth observation datasets) with advanced crop and environment
models to provide actionable on-farm decisions.
Trial 2 results
This DataBio tool has been developed specifically for two Irrigation Communities as the final
customer. In the current pilot the objective was the water and energy saving by the use in
irrigation areas using the following techniques:
• EO Big Data sources and Remote sensing applications for calculate NDVI and corrected
crop factor Kc and balanced against participation, which needs to be measured on site.
• RPAS for address specific problems: plagues and diseases, irrigation uniformity, soils
problems etc.
• Agroclimatic stations and IoT sensors to provide information in situ.
• Telecontrol systems.
• Irrigation equipment.
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Figure 61: Comparative Kc obtains for remote sensor in front FAO data per cereal
This DataBio pilot aims to reinforce agribusiness sector adapting the diversification of
production to new economic and climate scenarios to systems of remote control and remote
management in irrigated areas where is essential to achieve smart agriculture based Big Data
sources.
An algorithm C5.0 (R language) has been used for the automatic classification of soil cover,
which allows the generation of decision trees combining data of different types (cartography,
images, BD, etc.). It must be taken into account that these will vary according to the zones,
their availability and quality. The classification algorithm was trained using samples of the
different uses and coverages to be identified. The sample data are divided into two groups,
in this way 80% of the samples are used in the construction of the model. Once the decision
tree is generated, it is validated with 20% of remaining samples, not used in its construction.
Using samples from all land uses, a decision tree is generated from which a classification of
large LPIS uses is obtained. Using only the agricultural samples, another tree is generated,
from which a classification of agricultural crops is obtained. The combination of both
classifications will result in the crop layer and soil cover.
After this technical description of the algorithms, it is necessary to emphasize that they had
(in the development phase) the following limitations: (i) in LPIS there are no differences
between arable crops, so it is not possible to verify if the crop identified by remote sensing
coincides with the one existing in the field and (ii) the spatial resolution of Sentinel-2 does not
allow the correct identification of woody crops, since it is limited to the response of the crop
and / or the plant coverings under it.
The results will collect:
• Null match: agricultural use in LPIS. Classified as non-agricultural.
• Average coincidence: when both in LPIS and in the layer generated, the use is
agricultural.
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• High coincidence: when both in LPIS and in the layer generated, the crop is of the same
type (in both cases, it is a woody crop, or both are herbaceous).
• Perfect match: when the crop is the same in both LPIS and the layer generated by
remote sensing.
The cause of the discrepancies should be analysed with the supervision of photo interpreters,
with Sentinel-2 images being an important aid for this. Once defined the kind of crop, the
developed methodology allows, using temporal series of Sentinel data, the definition of Kc
parameter and, using it, the irrigation needs of the specific crop.
Figure 62: Result: High-Scale vigour map
As it is possible to see in the previous image, this DataBio System has obtained the temporal
evolution of specific plots to determine both water needs and vigour maps.
Finally, a methodology for the calculation of water needs has been developed and applied to
the Genil-Cabra (Andalusia) pilot zone. The farmer association involved in the pilot has
provided it with data on irrigated plots and crops from 2017. In addition, the pilot has used
Sentinel 2017 images. Using those datasets as initial Big Data sources, a classification process
has been developed to obtain the NDVI (Normalized Difference Vegetation Index). This
biological index is the basis for the calculation of water needs. In the final cycle of the project,
an integration data process has been carried out to harmonize and unify the different
datasets. The following image highlights how using all the previously explained processes is
possible to classify the plots accordingly to irrigation needs:
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Figure 63: Crops classification and irrigation needs
The maps, data sources and results obtained can be accessed through the component C11.01.
Components, datasets and pipelines
The development of a management application C11.01 has been completed. This tool allows
the aforementioned Big Data sources to be accessed in a useful way both by the managers of
the irrigation communities and by the farmers themselves. Figures 64 - 65 shows the current
status and general appearance of the web management application:
Figure 64: Management profile - Irrigation needs of the whole irrigation community
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Figure 65: Farmer profile - Irrigation needs for a specific parcel and crop
Moreover, a general service has been developed (with irrigation communities as customer)
to allow the publication of vigour maps and water needs of all the plots at provincial level. It
has been also completed the analysis of the utilization of conventional sensors, IoT sensors
and tools by ATOS and IBM Israel (integrated in the DataBio platform) besides with the
publication of the processed and generated information generated through viewers or end-
users.
Massive and rapidly updated data, bio-agronomic data, sensor data, terrestrial observation
data and geographic data from different sources were used, specifically:
• SENTINEL-2: are terrestrial observation data owned by the ESA
• Ortophotos: terrestrial observation data in image format obtained from the National
Geographic Institute of Spain.
• RPAS: are terrestrial observation data obtained by thermal and multispectral sensors,
owned by TRAGSA.
• SigPAC (LPIS): Spatial data in the ESRI Shapefile format which identify the parcels,
owned by Junta de Castilla y León.
• Alphanumeric data from surveys and field visits, owned by TRAGSA.
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Figure 66: Raspberry unit and IoT sensors
In regard to Big Data processing, the used remote sensing data such as Sentinel-2B have an
average TB size per year, the Spanish LPIS system has a size of hundreds of GBs, in the same
way as the Spanish orthophotos project (PNOA), as described in 7.2. Additional research has
been carried out on the acquisition of own data through sensors or IoT with the help of ATOS
and IBM Israel. After the capture and initial collection of data, storage and processing has
been conducted as described in 7.2, producing raster images and spatial databases.
As a first step, the assessment of the required satellite images and cloud processing service
platforms, part of DataBio platform, has been carried out. This evaluation was made in order
to obtain full functionality in web applications based on high frequency, scalable satellite
image data at national level. For this tool, data flow has been defined as presented in Figure
67.
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Figure 67: Data flow diagram of the model for the implementation of precision agriculture techniques
As a first step, work is being done to improve the radiometry of aerial orthophotos (PNOA),
in order to increase their homogeneity and their subsequent possibilities of use, both for
agrarian and environmental purposes, in automatic processes of image analysis together with
images from satellite (radiometric intercalibration, DataBio Component C11.03). For this,
several algorithms are being developed that allow radiometry to improve and visual
interpretation of orthophotos (adjustment of colors and levels). Moreover, a software (called
"Image Enhancer Framework") has been developed that allows applying these processes to
large amounts of aerial images.
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Figure 68: Definition of histograms. Result of homogenization of images
Regarding this topic, an operational methodology was designed for the generation of a layer
of ground cover and changes, using remote sensing, Big Data and GIS techniques,
methodology that has been proved functional for updating crop maps and crop health status
maps. An automatic sorting algorithm of machine learning type has been tested that
combines the temporal series of Sentinel-2 images with reference data from different sources
(PAC Declarations, SIGPAC, Forest Map, etc.). From this scope, four thematic layers have been
designed:
• Large-scale dataset: this set of raster data identifies the major land uses: agricultural,
forestry, pasture, unproductive, water and urban.
• Change dataset: The changes observed are grouped into 3 classes: change, doubt and
no change.
• Crop dataset and soil cover: this raster dataset is generated on the agricultural land
mask of LPIS. It supposes the maximum level of disaggregation of coverages / crops /
land uses to be achieved in each zone, according to the reference data used.
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• Dataset of discrepancies between the CAP declarations and the crop dataset obtained
by remote sensing.
DataBio component deployment status
Component
code and
name
Purpose for pilot Deployment status Component
location
C11.01 Pilot itself: tools, EO
processing algorithms,
WEB API developed
Completed TRAGSA
Group
C11.03 Radiometric improvement
of Orthophotos provided by
the National Geographic
Institute. This improvement
and physical features
harmonization (colour,
intensity…) allows this
datasource to be used with
similar accuracy than
Satellite Images.
Completed TRAGSA
Group
C05.02 IoT Hub is a middleware
component to support
continuous data collection
from IoT based resources.
B1.1 Pilot collects field Data
using IoT sensors which
information is gathered by
IoT Hub.
ATOS
C19.01 Complex event processing
engine for event stream
processing. The information
centralized by C05.02 is the
input for this component. It
stores the rules defined by
the final users of TRAGSA-
TRAGSATEC
IBM
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Data Assets
Data Type Dataset Dataset
original
source
Dataset
location
Volume (GB) Velocity
(GB/year)
Raster SENTINEL-2 ESA.
European
Space
Agency.
https://senti
nel.esa.int/w
eb/sentinel/
missions/sen
tinel-2/data-
products
~ TB ~ TB/year
Raster PNOA
(Spanish
National
Plan of
Ortophotos
)
IGN. National
Geographical
Institute.
http://centro
dedescargas.
cnig.es/Centr
oDescargas/b
uscadorCatal
ogo.do?codF
amilia=02211
~ GB Updating
frequency ~ 5
year
Raster RPAS - UAV Developed by
TRAGSA with
its own
drones fleet
N/A ~ GB On demand
Vectorial
(Shapes)
LPIS -
SIGPAC
Autonomous
Communities
http://www.i
decyl.jcyl.es/
geonetwork/
srv/spa/catal
og.search#/h
ome
~ MB Updating
frequency ~
2/3 year
Alphanumeric IoT Sensors TRAGSA IoT Fiware Small Low
Exploitation and Evaluation of pilot results
Water scarcity is an increasing and common worldwide phenomenon. Hydrologic cycles do
not coincide with the annual seasons and there are alternating periods of severe drought with
periods of heavy rains. As a general approach, irrigation agriculture is vital to guarantee food
security conditions to assure the well-being and progress levels demanded by European
Citizenship in the 21st century.
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According to FAO estimates, in the first decade of this century, the 17% of irrigated arable
crops supplied 42% of food in the world. By 2020, these irrigated arable crops are expected
to provide 50% of food using less water.
Therefore, sustainability of irrigation areas must be promoted, and it is mandatory to solve
their specific problems in order to meet their needs. The specific problems are: 1) water
scarcity, 2) increase of energy used, 3) absence of tools than determining the specific
requirements of each crop at the time, 4) lack of generalized and interoperable tools, 5) water
quality problems, 6) lack of performance of irrigation arable crops, 7) lack of research in the
process of switching to alternative crops: develop pest-resistant local crop varieties, develop
crop with low water requiring, etc., 8) no control of needs required to optimize the work.
Therefore, the overall challenge is to get a smart agriculture to ensure optimal conditions. It
will be necessary to get social and environmental challenges in order to attend the needs in
irrigation areas and turn them into optimized production areas.
Therefore, the following social challenges should be considered:
Sustainable Production: 1) Selecting better seeds than increase the productivity to attend the
increase of demand of food in a limit surface. Selection process and genetic improvement will
get better agricultural performance and the stabilization of this production. 2) Water
management for security agriculture and economically viable. The use of innovative
technologies, as Big Data, to design new software it is necessary to get an optimum use of
water in agriculture. 3) Fertilization optimum to use technologies to know the availability of
nutrients of the soil. The technologies used are Earth Observation EO, models or soil sensor
than will help to mechanics of land regulation to maintenance the plant nutrition, 4) Technical
process to get the best quality in soils. It will be necessary to use EO, models, soil sensors,
machinery etc for identification of problems and establish preventive measures, recovery
and/or control and monitoring necessary to implement on the ground in order to improve
their environmental conditions and remove, if any, risks than may result from contamination
having said soil.
Cost: Water scarcity and increasing energy costs are the most important threats to irrigated
agriculture. All agents involved in this sector are worried about these challenges which
require the integration of continuous sustainable technological innovation and new
management structures to achieve improved water and energy efficiencies in each region.
Furthermore, these problems could be transferred to the agribusiness sector, due to the need
for security, stability and warranty in raw material supply, created around the irrigated areas.
On the other hand, in many cases, there is the possibility of clean and renewable energy
sources introduction. It will reduce the costs in energy of irrigation areas.
Risk: The health security and safety in food is a big preoccupation. It is necessary to guarantee
the security and safety in food production. The use of unmanned aerial vehicles (UAV
technologies) allows pest and disease control. In addition, these technologies contribute
additional information which may help to distinguish the best variety in each area or the
elaboration of varieties with resistance to pests and diseases.
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Collective decision making: To support farmers ́ decision making in relation to the use of
these resources (water, manure and fertilizers) and their management strategy of these
resources.
The DataBio B1.1 pilot has used different kind of sensors, and actuators distributed in
Irrigations Communities in experimental facilities for testing and finally in real scenarios
dealing with daily activity and real impact on advances in services and infrastructures that are
in place for systemic innovation in Water Communities. The kind of sensors and actuators are
very similar in all the modernization irrigation areas and the number varies depending on the
considered scenario.
These technologies contribute to smart agriculture, so that through them the right amount is
watered in getting the optimum time to apply water efficiency criteria that contribute to
improving food security, in the sense that if the amount is increased available water potential
production increases.
Pilot exploitation based on results
The final service provides information for precision agriculture, mainly based on time series
of high resolution (Sentinel-2 type) satellite images, complemented with IoT sensor data and,
in some specific cases defined by profitability, with RPAS data. The final costs saving for
farmer communities due to a better-quality management in agricultural zones, especially
focused on irrigated crops, are produced, mainly, by a water and energy better management.
Besides this, fertilizers control and monitoring produce, eventually, a prominent economic
saving per year and hectare. This better management of hydric and energetic resources is also
related to Green-house effect gases reduction, directly linked to better environmental
conditions in agriculture.
As a summary, Spain has an area of 3.621.722 hectares for irrigated agriculture, of which 73%
is modernized irrigation pressure and the remaining 27% is irrigated by gravity. Many of them
are managed under the control of Irrigation Communities; they would be our addressable
market.
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KPIs
KPI short
name
KPI
description
Goal
description
Base
value
Target
value
Measur
ed value
Unit of
value
Commen
t
Surface Processed
Surface
2 Irrigation
Communiti
es
4000 12499
.87
36445.8
7
Ha
Tool Water
needs tool
0 1 1 Tool Web API
and Web
service
develope
d
Final
users
Number of
users
Stakeholder
s using the
tool
0 10 300 user
Campaig
n
Irrigation
campaign (in
Real
conditions)
managed by
the tool
1
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8 Pilot 6 [B1.2] Cereals, biomass and cotton crops_2
Pilot overview
The main focus of this pilot is to offer smart farming advisory services dedicated for arable
crops, based on a set of complementary monitoring and data management technologies (IoT,
EO data, Big Data analytics). Smart farming services are offered as irrigation advices through
flexible mechanisms to the farmers or the agricultural advisors. The pilot will target towards
exploiting heterogeneous data, facts and scientific knowledge to facilitate decisions and their
application in the field. It will promote the adoption of Big Data enabled technologies and will
collaborate with certified professionals to better manage the natural resources and
specifically the use of fresh water. NP is leading the pilot activities with the support of GAIA
EPICHEIREIN and Fraunhofer for the execution of the full lifecycle of the pilot. The pilot
activities are being performed at Kileler, Greece in an area covering 5000ha and the targeted
arable crop is cotton.
Figure 69: Pilot B1.2 high-level overview
In order to support the business expansion of the Big Data enabled technologies that are
introduced within the present DataBio pilot, NP and GAIA EPICHEIREIN have already
established an innovative business model that allows a swift market uptake. With no upfront
infrastructure investment costs and a subscription fee proportionate to a parcel’s size and
crop type, each smallholder farmer, can now easily participate and benefit from the
provisioned advisory services. Moreover, and as more than 70 agricultural cooperatives are
shareholders of GAIA EPICHEIREIN, it is evident that there is a clear face to the market and a
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great liaison with end-user communities for introducing the pilot innovations and promoting
the commercial adoption of the DataBio’s technologies.
Summary of pilot before Trial 2
The pilot has completed the first round of trials during Trial 1. It effectively demonstrated how
Big Data enabled technologies and smart farming advisory services can offer the means for
better managing the natural resources and for optimizing the use of agricultural inputs (fresh
water). All these assumptions have been validated through a set of pilot KPIs which met the
targeted expectations (documented in D1.2). This has been achieved as farmers and the
agricultural advisors showed a collaborative spirit and followed the advices that were
generated by DataBio’s solutions.
Preparation and execution of Trial 2
Trial 2 timeline
The following roadmap applies for this pilot.
Figure 70: Pilot B1.2 timeline
Preparation for Trial 2
The following work was conducted by NP, as part of the preparatory work for Trial 2.
As the requirements in terms of sensors deployed for in-the-field usage differ between pilot
sites, it became obvious that several adaptations were necessary in respect to C13.03 and the
way data was represented for both cloud-based storing and Gaiatron station configuration.
More specifically, all relational and EAV (Entity-Attribute-Value) data representations were
adapted to more flexible and scalable JSON format that performs better in a dynamic IoT
measuring environment. The latter is widely acknowledged as JSON has become gradually the
standard format for collecting and storing semi-structured datasets that originate from IoT
devices. The adaptation to a JSON format for modelling IoT data streams allows the further
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processing, parsing, integration and sharing of data collections in support of system
interoperability though the adaptation on well-established and favoured linked-data
approaches (JSON-LD).
User Interface integration was performed so that the farm management portal (holding all
data of agronomic value and the embedded DSS serving as the endpoint for providing the
advisory services) is integrated with the farm electronic calendar (the endpoint where the
farmer or the agricultural advisor ingests information to the system regarding the applied
cultivation practices, field level observations, sampling, etc.). Both these tools were
developed using the component C13.01. Integration activities were conducted in order to
offer a seamless user experience and allowing the user to carry out his/her intended
operations without going back and forth across different systems.
Figure 71: Screenshot of the unified UI developed for Trial 2. The red menu item indicates farm log functionalities while the orange menu item the farm management functionalities respectively
A new mobile application was developed, namely “gaiasense Field Collect”, so that field-level
data collection can be performed through an Android-powered device. Lessons-learnt from
Trial 1 indicated that by using portable smart devices, it would be easier for the farmer or the
agricultural advisor to ingest data into the system (farm and eye data dimensions as indicated
in Figure 1). The application was implemented with the purpose of supporting several
functionalities like:
a) detailed planning and control of the process of trapping and monitoring of the
population and the spread of insect infestation within a crop. More specifically,
farmers have the ability to record insect infestation directly on the field with the help
of a smartphone and use this data to more effectively control the damage caused by
enemies while reducing the amount of insecticides released into the soil,
b) the recording of the phenological stage of the cultivation at the time of the field
inspection,
c) the recording of soil samples from points within the field, irrigation measurements,
and of cultivation symptoms mainly from enemies and diseases.
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Figure 72: Screenshots of the android app used for collecting farm data
Daily evapotranspiration is considered a critical parameter for generating irrigation advices.
It essentially reflects the water content being lost each day from both the plant and the soil.
By calculating this parameter using EO or modelled approaches, the requirement for installing
a tense network of irrigation sensors for monitoring soil moisture ceases to exist. This
significantly reduces infrastructure costs and leads to economy of scale, as irrigation advices
can be extrapolated for many parcels that share similar agro-climatic characteristics (soft
facts). Within Trial 2 preparatory phase, a modelled-based approach has been explored that
attempts to simulate the operation of a high-end pyranometer while measuring the solar
irradiance – an input parameter for reference evapotranspiration. ML methods (neural
networks) have been applied correlating EO and sensor data (from both low cost and high-
end sensors) in order to generate highly accurate, low cost reference evapotranspiration
measurements even at parcel level (Figure 73). The results are encouraging showing an
accuracy of up to ~90% in estimating solar irradiance by fusing low-cost sensor measurements
with EO data. This constitutes a major innovation of the pilot as it sets the stage for significant
infrastructure cost reduction that will make Smart Farming approaches even more accessible
and appealing for adoption by the farmer communities.
The preparatory work conducted by FRAUNHOFER for Trial 2 concentrated on the discussion
how existing analytic services could be integrated into a web-based analytic-platform with
ease. The starting point for this was provided by the solution developed during Trial 1. During
this discussion, a variety of ideas were developed how different services could be integrated
into a single platform, which is also able to cope with multiple data sources, to fulfil the needs
of specific use-cases. One of the major challenges of such efforts is to reduce the complexity
of integration. Principles of modern architecture styles such as Self-contained Systems (SCS)
(https://scs-architecture.org) or Microservices were considered to promote the separation
into independent components. Each component consists of capabilities to access data,
process or analyse it and consequently visualize the result. The integration itself must be done
at the UI-Layer. Following this approach provides more flexibility and eventually allows
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thinking about a platform which enables the users to build views for custom analytic tasks
composed by a variety of components. The horizontal impact of this stage can provide
solutions for multiple scenarios spanning from Smart Farming, to CAP support and Agri-
Insurance.
The implementation of Trial 2 focuses primary on the integration of external services. A
variety of visual analytic tools are included to allow efficient exploration of available data. The
integration of services and data sources is done using well-defined RESTful interfaces.
Trial 2 execution
During Trial 2 the following actions have been performed by the partners involved in the pilot
activities:
By M26, the growing season starts. Moreover, DataBio platform v2 for the pilot is fully
operational and involves offering to the farmers and the agricultural advisors technological
tools (unified UI and “gaiasense” field collect android app) in order that they provide
feedback, measurements, observations, and detailed data regarding the farming practices.
Especially, in respect to the farming practices information needs to be ingested into the
system at regular intervals (once a week). As the farming ecosystem is really complex, it is
essential to capture this information at this level of detail in order to shape a complete view
of the monitored parcels. NP was in charge of supervising the data collection process.
Moreover, certified agricultural advisors are starting to use the aforementioned main pilot
UIs in order to access the full set of collected data (in situ agro-climate, EO-based,
crowdsourced, modelled, machine-generated), evaluate it and offer data-driven advices to
the farmers towards better resource management, improved products and yields (more
descriptions and figures can be also found in Deliverable D1.2).
Some indicative figures from the pilots are presented in Figures 73 - 75.
Figure 73: Parcel monitoring at Kileler pilot site indicating some slight intra-field variations in terms of vegetation index (NDVI) and cross-correlations among the latter with ambient temperature and rainfall (mm)
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Figure 74: Reference evapotranspiration monitoring at Kileler (both modelled using ML methods developed by NP and based on Copernicus EO data) for July 2019
Figure 75: Irrigation monitoring at a Kileler pilot parcel showing one (1) correct irrigation (water drop icon) after following the advisory services. The impact of rainfalls in the soil water content is obvious on several occasions and if translated correctly can prevent unnecessary irrigations
By M28, a preliminary architecture for FRAUNHOFER’s analytics platform has been drafted.
The platform was the main discussion topic during the M28 DataBio Thessaloniki Codecamp,
hosted in NEUROPUBLIC’s N.Greece offices with the participation of other DataBio partners
involved in the WP1 pilots led by NEUROPUBLIC. Furthermore the generalization and simple
adaption to other scenarios was discussed intensively.
By M34, the growing season ends and final KPI measurements are collected. More specifically,
from regular discussions with the farmers and the agronomists/agricultural advisors involved
in the pilot activities, final KPI measurements and feedback was collected and can be found
in Section 8.5.2. This work was conducted by NP and GAIA EPICHEIREIN.
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Trial 2 results
In Trial 2, the applied technologies and pipelines got even more mature and reached their
expected TRL. The farmers and their agricultural advisors continued (for a second year) to
benefit from irrigation advices aiming to facilitate the decision-making process and optimize
the use of agricultural inputs. The collected KPIs validate the pilot assumptions.
It is effectively shown that the results pretty much aligned with the initial set targets for
irrigation cost reduction (Figure 76). This is due to the fact that the farmers both showed
collaborative spirit and adapted their farming practices using the advice offered, thus,
reducing the freshwater requirements during critical phenological stages of their crops.
Figure 76: Aggregated results of the pilot in comparison with the target values
Components, datasets and pipelines
DataBio component deployment status
Component code
and name
Purpose for pilot Deployment status Component
location
C13.01
Neurocode (NP)
Neurocode allows the
creation of the main
pilot UIs in order to be
used by the end-users
(farmer, agronomists)
and offer smart farming
services for optimal
decision making
deployed NP Servers
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C13.03 GAIABus
DataSmart Real-
time streaming
Subcomponent
(NP)
Real-time data stream
monitoring for NP’s
GAIAtrons
Infrastructure installed
in the pilot site
Real-time validation of
data
Real-time parsing and
cross-checking
deployed NP Servers
C04.02 – C04.04
Georocket,
Geotoolbox,
SmartVis3D
(Fraunhofer)
Back-end system for Big
Data preparation,
handling fast querying
and spatial
aggregations (data
courtesy of NP)
Front-end application
for interactive data
visualization and
analytics
deployed Fraunhofer
Servers
Data Assets
Data Type Dataset Dataset
original source
Datase
t
locatio
n
Volum
e (GB)
Velocity
(GB/year)
Sensor
measuremen
ts (numerical
data) and
metadata
(timestamps,
sensor id,
etc.)
Gaiasense
field. Dataset
composed of
measurement
s from NP’s
telemetric IoT
agro-climate
stations called
GAIATrons for
the pilot site.
NEUROPUBLIC GAIA
Cloud
(NP’s
servers
)
Severa
l GBs
Configurable
collection and
transmission
rates for all
GAIATrons. 4
GAIAtrons
fully
operational at
the pilot sites
collecting >
30MBs of data
per year each
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with current
configuration
(measuremen
ts every 10
minutes)
EO products
in raster
format and
metadata
Dataset
comprised of
remote
sensing data
from the
Sentinel-2
optical
products (1
tile)
ESA
(Copernicus
Data)
GAIA
Cloud
(NP’s
servers
)
>1000 >350
Exploitation and Evaluation of pilot results
Pilot exploitation based on results
NP and GAIA EPICHEIREIN have already launched on 2013 their Smart Farming program, called
“gaiasense” (http://www.gaiasense.gr/en/gaiasense-smart-farming), which aims to establish
a national wide network of telemetric stations with agri-sensors and use the data to create a
wide range of smart farming services for agricultural professionals.
Within the DataBio the quality of the provided services greatly benefited from the
collaboration with leading technological partners like Fraunhofer, that specializes in the
analysis of Big Data. Moreover, feedback from the end-users and lessons-learnt from the pilot
execution significantly fine-tuned and will continue to shape the suite of dedicated tools and
services, thus, facilitating the penetration of “gaiasense” in the Greek agri-food sector.
The sustainability of NP’s DataBio-enhanced smart farming services, after the end of the
project is achieved through: a) the commercial launch and market growth of “gaiasense” and
b) the participation to other EU and national R&D initiatives. This will allow continuously
evolving/validating the outcomes of the project, by working with both new and existing (to
DataBio) user communities and applying its innovative approach to new and existing (again
to DataBio) areas/crops.
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KPIs
KPI
short
nam
e
KPI
description
Goal
descriptio
n
Base
value
Target
value
Measure
d value
Unit
of
value
Comment
B1.2
_1
Reduction
in the
average
cost of
irrigation
per hectare
following
the
advisory
services at a
given
period.
2670 1869 1881 euros
/ha
B1.2
_2
Decrease in
inputs
focused on
irrigation
(amount of
water used)
2670 1869 1881 m3/h
a
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9 Pilot 7 [B1.3] Cereal and biomass crops_3 Pilot overview
This pilot was designed to implement remote sensing, IoT farm telemetry and proximal sensor
network-based Big Data technologies for biomass crop monitoring, predictions, and
management in order to sustainably increase farming productivity and quality, while at the
same time, minimizing farming and environment associated risks. Biomass crops of interest
include biomass sorghum and cardoon, which can be used for several purposes including,
respectively, biofuel, fiber, and biochemicals, with a high macroeconomic impact. Fiber hemp
was anticipated but, due to unexpected farmers aversion, this crop was not included in pilots.
The aversion was particularly triggered by a complicated market of the produce. Similarly, the
IoT farm telemetry technology was used in year one for a preliminary observation but, this
technology revealed itself ill adapted to biomass sorghum as the hardware, particularly the
cables, were frequently damaged by rodents. IoT was therefore removed from the trial
settings as frequent repairs were becoming a burden. The offered smart farming services
include Biomass crop monitoring using proximal sensors to derive vegetation indices, and
crop growth and yield modelling using fAPAR derived from satellite (Sentinel 2A and 2B)
imagery and appropriate machine learning techniques. The pilot secured adhesion of private
farmers and/or farming cooperatives. During the 2017 and 2018 cropping seasons, 43
sorghum pilots were run covering 240 hectares. The work on this pilot was distributed
between CREA, Novamont, and VITO. CREA worked on sorghum, and Novamont on cardoon.
VITO supported remote sensing technologies, while CREA supported proximal sensor
technology.
During 2018 an additional field of cardoon was included in the monitoring in Umbria Region
beyond the one already included in the previous reports in Sardinia, in order to give an
example of different cultivation area and cover some of the main areas where cardoon can
be cultivated. In 2018, in collaboration with InfAI, CREA was able to extend crop monitoring
to foliar diseases in one of the pilot field in Anzola, Italy. The goal was to evaluate to
possibilities of crop disease detection from Earth Observation products. For this investigation,
R-CNN - a Regional Convolutional Neural Network was implemented. Despite the great
potential we uncovered in the disease monitoring technology, we nonetheless identified a
weakness associated with relying heavily on natural disease inoculum. Indeed, natural
inoculum is heterogeneous in the field and diseased areas can range from a single plant to a
few plants which is greatly challenging in terms of resolution. This investigation was therefore
discontinued in 2019.
In 2019, crop monitoring activities in biomass sorghums continued in collaboration with VITO
and the agriculture cooperative CAB MASSARI. Four pilots were established in 2019 as
depicted in the below table.
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Figure 77: Sorghum pilots established in 2019
Summary of pilot before Trial 2
In terms of global sorghum crop disease monitoring, five training and testing fields for crop
disease detection had been identified by CREA. Within this diseased field, CREA delimited a
most diseased area of about 1000 square meters (~232 m of perimeter) within which leaf
disease occurred in about 60 to 70% of the plants. Two foliar diseases were observed, i.e.,
Anthracnose (most prevalent) and Bacterial stripe. The primary hypothesis is that most crop
diseases highly correlate with the chlorophyll content of the crop. Moreover, the chlorophyll
content can be measured by multispectral images. Therefore, the NDVI (Normalized
Difference Vegetation Index) has been used. In the first run, excellent results had been
developed. The network works as it should and detect the fields (Figure 78).
The network was even able to detect the disease and distinguish it from surrounding areas
(Figure 79).
Figure 78: Sorghum Foliar Diseases Detected area with the reliability of 0,925
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Figure 79: Sorghum Foliar Diseases Detected area with the reliability of 0,861
The set was very small. Overall there were six training sets and two for validation, so the
results were limited. The main problem of small datasets is the overfitting – which means that
the models are trained too well, precisely to the set of data. In order to overcome overfitting,
we are working on the following issues:
• Expand the database (contact to Saxonia local agricultural government, more will
follow)
• Augmentation (Expand the database by manipulation)
• Regulation
Up to now, we created 1000 test cases out of our starting point, and the success rate is still
high.
For the crop monitoring using satellite imageries, forty-three pilot biomass sorghum trials
were run by CREA over two cropping seasons in 2017 and 2018 as represented in Figure 80.
The biomass sorghum pilot trials were mainly established in private farms and co-run by CREA
and private farmers and private farming cooperatives operating in the northern Italian
communes of Nonantola, Mirandola, and Conselice. Only eight pilots were run in CREA’s
experimental station of Cà Rossa (Anzola dell’Emilia) in both 2017 and 2018 cropping seasons.
During the 2018 cropping season, sorghum was monitored for phenology, yields, and foliar
diseases. Two cardoon fields were monitored in 2018, one located in the North of Sardinia,
as continuation of 2017 work, this field cardoon was established in 2014. The other field is
located in Umbria, which represents a quite new area for the cardoon and where breeding
activity is also carried out by Novamont. In the last cultivation period (2017-18) in Umbria the
phonological phases were monitored together with the agronomical operations.
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Figure 80: Map of Italy (A) with a rectangle inset indicating the geographical location of the experimental sites (red dots) for pilots established in 2017 (B) and 2018 (C)
Preparation and execution of Trial 2
Trial 2 timeline
January - May 2019: Pilot sites identification, preparing contracts between CREA and the
farming cooperative CAB MASSARI of Conselice, Italy, preparing fields and calibrating seeds,
sowing the pilots.
May - October 2019: Field visits, data collection, Data processing, and reporting.
Preparation for Trial 2
In collaboration with the farming cooperative CAB Massari of Conselice, the pilot sites were
identified, and ad hoc contract signed between CREA and CAB Massari. The contract
described the sequence of field activities that CAB Massari and CREA had to carry out in the
pilots. The plot sites were geolocated and the coordinates entered into VITO system for
monitoring the fAPAR index throughout the cropping season. In addition, Chlorophyl meter
and NDVI meters were prepared for respective data collection.
Trial 2 execution
Chlorophyl index and NDVI index were collected weekly. Fields were geolocalized,
geolocation data saved as kml files before they were integrated into WatchItGrow®
application. Sentinel-2A and Sentinel-2B images from tile 32TQQ were downloaded from ESA
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and processed. Processing included atmospheric correction with iCOR, cloud and shadow
detection using Sen2COR v2.5.5 and calculation of biophysical parameters using BV-NET
(Biophysical Variable Neural Network). The BV-NET methodology is based on neural networks,
which are trained on a synthetic dataset of around 50000 simulations using the PROSAIL
model. Both Sen2Cor and BV-NET are made available through ESA’s SNAP (Sentinel
Application Platform) toolbox. In this study, fAPAR was used to estimate biomass yield. The
fAPAR estimates were generated at decametric spatial resolution (10m pixel size), and a
temporal resolution of 5 days up to 2-3 days in those areas where the different satellite
overpasses overlapped. Spatial resolution refers to the surface area measured on the ground
and represented by an individual pixel, while temporal resolution is the amount of time,
expressed in days, that elapses before a satellite revisits a particular point on the Earth's
surface. For each experimental field, fAPAR or “greenness” maps were produced, and a
growth curve was built, showing the evolution of the fAPAR values throughout the cropping
season. To correct for artefacts in the curve such as abnormally low fAPAR values due to
undetected clouds, shadows or haze and to interpolate fAPAR values between subsequent
acquisition dates, a Whittaker smoothing filter was applied on the curve. Finally, the fAPAR
values from the curves were used for further analytics.
Four models were assessed including simple linear model (LM), Bayesian additive regression
trees (bartMachine method), Bayesian generalized linear model (bayesglm method), and
eXtreme Gradient boosting (xgbTree method). The simple linear model was used as a
benchmark to gauge the performance of the models implemented. The models evaluated
were selected based on their robustness. Fortnightly fAPAR values acquired from late April to
late August were used in this work, resulting in nine days of year (DOY) that is, from DOY 120
in April to DOY 240 in August. These days of year were used as explanatory (regressors)
variables in successive predictive modelling of sorghum biomass yields. The dataset was
randomly partitioned into training (80% of the entire dataset) and testing set (20% of the
entire dataset). The training set was used to run a cross-validation experiment to train and
assess the models using a 10x repeated 5-random fold cross-validation (CV), rendering a total
of 50 estimates of accuracy and prediction error. Models were validated on the testing set
which was an external test (validation) sample. The models were evaluated based on the
coefficient of determination (R2), mean absolute error (MAE), mean absolute percentage
error (MAPE), and symmetrical mean absolute percentage error (SMAPE). The MAPE makes
it possible to compare the prediction of different dependent variables that were evaluated
using different scales. The MAE measured the average magnitude of the errors in the set of
predicted values without considering their direction. The MAE provides an unambiguous
measure of the magnitude of the average error and is therefore more appropriate than the
Root Mean Square Error (RMSE) for dimensioned evaluations of aver-age model performance
error. The symmetrical MAPE (SMAPE) was used to deal with some of the limitations of the
MAPE. As in MAPE, SMAPE averages the absolute percentage errors but these errors are
computed using a denominator representing the average of the forecast and observed values.
SMAPE has an up-per limit of 200%, that is a 0 to 2 range that is useful to judge the level of
accuracy and that should be influenced less by extreme values. Furthermore, SMAPE corrects
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for the computation asymmetry of the percentage error. The MAE built within the repeated
cross validation procedure was used to assess the dependability of the model performance.
On the other hand, all the above metrics as obtained on the testing set were used to assess
the model predictive ability. The importance of the explanatory variables (useful prediction
times) was determined using a 0 to 100 index, with 0 no effect and 100 the highest magnitude
of the regressor’s importance.
Trial 2 results
The results obtained in the Trial 2 (third year of the project) were integrated with the previous
two years’ data in order to be meaningful. The MAE dispersion during training experiment
was increasingly narrower in the order LM > bayesglm > xgbTree > bartMachine methods.
Over the months evaluated, the prediction errors in the testing set were mostly higher with
the linear model, which also displayed the least value of the coefficient of determination
(Table 7). Overall, the bartMachine method showed relatively high R2 values and least values
of prediction errors. The best regressors were D.150 (second half of May) and D.165 (first half
of June) (Figure 81). D.240, D.195, D.210, and D.120 showed minor effects, while D.135,
D.180, and D.225 showed no prediction importance.
Table 7: The observed performance of implemented models.
Model SMAPE
(%)
MAPE
(%)
MAE
(t ha-)
R2
LM 0.74 0.99 10.47 0.47
bartMachine 0.18 0.16 2.32 0.51
Bayesglm 0.74 0.98 10.34 0.48
xgbTree 0.44 0.36 4.07 0.62
SMAPE, MAPE, MAE, R2, respectively, symmetrical mean absolute percentage error, mean absolute percentage error, mean
absolute error, and coefficient of determination. LM, bartMachine, bayesglm, xgbTree, respectively, simple linear model,
Bayesian additive regression trees (bartMachine method), Bayesian generalized linear model (bayesglm method), and
eXtreme Gradient boosting (xgbTree method).
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Figure 81: Left: visualization of models cross-validation MAE (t ha-1) dispersion using boxplot approach and fAPAR acquired from April to August. LM, bartMachine, bayesglm, xgbTree, respectively, simple linear model, Bayesian additive regression trees (bartMachine method), Bayesian generalized linear model (bayesglm method), and eXtreme Gradient boosting (xgbTree method). Right:Relative importance of regressors (day of year, D) on sorghum biomass yields using bartMachine method
The pilot B1.3 was conducted yearly from 2017 through 2019. An integrative analysis was
carried out that accounted for: 1) the data collected from the 2017 preliminary trials, 2) the
data collected from the 2018 Trial 1, and 3) the data collected from the 2019 Trial 2. An
integrative conclusion is therefore in order. Clearly, Sentinel-2-derived fraction of absorbed
photosynthetically active radiation (fAPAR) was found to explain primary productivity and
was used in this study as biophysical variable in the predictive modelling of aboveground
biomass yields in annual and perennial sorghums. Bayesian additive regression trees
(bartMachine method), a Bayesian machine learning approach, was found more promising
than most artificial intelligence approaches, and predicting sorghum biomass yields using as
regressors days of year 150 and 165 offered much modelling performance.
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Components, datasets and pipelines
DataBio component deployment status
Component code
and name
Purpose for pilot Deployment status Component
location
C12.03 EO4CDD
Detect crop
diseases, Tested
during trail stage 1
Initial set up InfAI
[Germany]
Server
https://www.d
atabiohub.eu/
registry/#servi
ce-
view/EO4SDD
C08.02 (Proba-V
MEP)
Sentinel-2
processing,
dashboards,
services for viewing
and time series
extraction
Adapted according to the
needs of pilot B1.3
Proba-V MEP
at VITO
C22.01 Crop monitoring
and yields
prediction
Adapted according the
history and events in the
pilot B1.3
CREA
(ephrem.haby
arimana@crea
.gov.it)
Data Assets
Data Type Dataset Dataset original
source
Datase
t
locatio
n
Volum
e (GB)
Velocity
(GB/year)
Phenotypic
data
Sorghum
biomass.CRE
A
CREA CREA 0.3x10
^-3
0.15x10^-3
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Geospatial
data
Sentinel.sorg
hum.CREA
VITO VITO 3000 3000
Optical
sensors data
NDVI.Chl.CR
EA
CREA CREA 3x10^-
3
3x10^-3
Exploitation and Evaluation of pilot results
KPIs
KPI
short
nam
e
KPI
descripti
on
Goal
description
Base
value
Target
value
Measure
d value
Unit
of
value
Comment
CREA
-
B1.3-
KPI-
01
Early
within
season
Yields
prediction
error
Reduce
prediction
error
5 5 0.16 % MAPE (%,
mean absolute
percentage
error)
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10 Pilot 8 [B1.4] Cereals and biomass crops_4 Pilot overview
The pilot aims to develop a platform for mapping of crop vigor status by using EO data
(Landsat, Sentinel) as the support tool for variable rate application (VRA) of fertilizers and
crop protection. This includes identification of crop status, mapping of spatial variability and
delineation of management zones. Development of platform is realized on the cooperative
8300 ha farm in Czech Republic, however basic datasets are already prepared for all Czech
Republic. So current status of pilot support utilisation of solution on any farm in Czech
Republic.
The pilot farm Rostenice a.s. with 8.300 ha of arable land represents a bigger enterprise
established by aggregating several farms in past 20 years. Main production is focused on the
cereals (winter wheat, spring barley, grain maize), oilseed rape and silage maize for biogas
power station. Crop cultivation is under standard practices, partly conservation practices is
treated on the sloped fields threatened by soil erosion. Over 1600 ha is mapped since 2006
by high density soil sampling (1 sample per 3 ha) as the input information for variable
application of base fertilizers (P, K, Mg, Ca). Farm machines are equipped by RTK guidance
with 2-4 cm accuracy. Farm agronomists don’t use any strategy for VRA of nitrogen fertilizers
and crop protection because of lack of reliable solutions in CZ.
The work was supported by development of platform for automatic downloading of Sentinel
2 data and automatic atmospheric correction. Currently is Lesprojekt ready to offer
commercial services with processing satellite data for any farm in Czech Republic
Other part was focused on transferring Czech LPIS into FOODIE ontology and to developed
effective tools for querying data. This work was done together with PSNC and system is
currently supporting open accessing to anonymous LPIS data through FOODIE ontology and
also secure access to farm data.
The main focus of the pilot is on the monitoring of cereal fields by high resolution satellite
imaging data (Landsat 8, Sentinel 2) and delineation of management zones within the fields
for variable rate application of fertilizers. The main innovation is to offer a solution in form of
web GIS portal for farmers, where users could monitor their fields from EO data based on the
specified time period, select cloudless scenes and use them for further analysis. This analysis
includes unsupervised classification for defined number of classes as identification of main
zones and generating prescription maps for variable rate application of fertilizers or crop
protection products based on the mean doses defined by farmers in web GIS interface.
Summary of pilot before Trial 2
As the result of Trial 1, spatial data about crop yields from harvester were recorded in the
period from June to September. From the total acreage of pilot farm 8.300 ha, more than
3350 ha of arable land was covered by yield mapping in the cropping season 2018. Especially
crop yields were recorded grain cereals (winter wheat, spring barley, winter barley), oilseed
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rape and also grain maize. Data was later processed for outlier analysis and by spatial
interpolation techniques to obtain final crop yield map in absolute [t.ha-1] and relative [%]
measure.
Figure 82: Yield maps represented as relative values to the average crop yield of each field (harvest 2018)
During the 2018 vegetation period, field experiment was established for testing variable rate
application of nitrogen fertilizer based on the yield potential maps computed from Landsat
time-series imagery and digital elevation model (DEM). Testing was carried out on three fields
with total acreage of 133 ha. The main reason was to tailor nitrogen rates for spring barley
according to the site-specific yield productivity and to avoid the crop lodging risk in the water
accumulation areas. Plant nutrition of spring barley for malt production is more difficult than
other cereals because of limits for maximal N content in grain. Thus, balancing of N rates to
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reach highest yield and simultaneously not to exceed N content in grain is crucial for
successive production of spring barley.
For definition of yield productivity zones, 8-year time-series of Landsat imagery data was
processed with the results of relative crop variability. Final map is represented as percentage
of the yield to the mean value of each plot, later multiplied by expected yield [t.ha-1] as the
numeric variable for each field and crop species. Values of yield potential were reclassified
into three categories – high, middle and low-yielded areas – nitrogen rate was increased in
the high expected yield areas.
To guarantee access for farmers and testing of yield potential we calculate yield potential for
2017 season on basic level for all Czech Republic and data are now available as Open on
Lesprojekt server for all Czech Republic. Farmers can test this basic data for their purpose
freely.
Figure 83: Transformation and publication of Czech data as Linked data with prototype system for visualising
Linked Data
PSNC contributed to this pilot with the transformation and publication of Czech data as Linked
data in order to provide an integrated view over different and heterogeneous data sources.
This work has been carried out by applying the pipeline described in D4.4 Section 3.3 (final
version), taking as input data from the pilot partners (farm data) as well as different open
Czech datasets, and by transforming them into Linked Data using FOODIE ontology (described
in D4.i1 Section A.15) as the underlying model. In particular, the following datasets were
transformed:
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• Farm data
○ Rostenice pilot farm data, including information about each field names with the associated cereal crop classifications arranged by year.
○ Data about the field boundaries and crop map and yield potential of most of the fields in Rostenice pilot farm
○ Yield records from two fields (Pivovarka, Predni) harvested in 2017.
• Open data
○ Czech LPIS data showing the actual field boundaries.
○ Czech erosion zones (strongly/SEO and moderately / MEO erosion-endangered soil zones).
○ Restricted area near to water bodies (example of 25m buffer according to the nitrate directive) from Czech.
○ The data about soil types from all over Czech.
These datasets were transformed into RDF format and published as linked data. The resulting
datasets (farm and open) are available as Linked Data in PSNC Virtuoso endpoint. In particular,
this work involved the following steps.
• Data modelling was one of the main tasks required to transform the input datasets
into RDF and to align them with the INSPIRE-based FOODIE data model (covering
farming and geospatial data). For this step, we took FOODIE ontology, which is based
on INSPIRE schema and the ISO 19100 series standards, as our base vocabulary and
created a Czech extension in order to represent all the farm and open data from the
input datasets. In particular the extension includes data elements and relations from
the input datasets that were not covered by the main FOODIE ontology and that were
specific to Czech partners needs
• Generation of the RDF data required a mapping file that specifies how to map the
contents of a dataset to RDF triples, matching the source dataset schema to FOODIE
ontology and extensions. This mapping file is generally an RDF document itself, written
in R2RML/RML, and includes information about the data source, its format and
connection details. Generating this mapping file is also not a trivial task, as most of the
available tools require manual editing of the R2RML11/RML12 definitions. The tool used
to execute the transformation usually also depends on the type of source data. As in
this experiment, both farm and open data were in the form of shapefiles, we used
GeoTriples13 tool in order to execute the mapping and generate RDF dumps from the
source shapefiles.
11 https://www.w3.org/TR/r2rml/ 12http://rml.io/ 13 http://geotriples.di.uoa.gr/
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• The RDF datasets that were generated were then loaded into Virtuoso triplestore. A
sparql endpoint and a faceted search endpoint are available for querying and
exploiting the Linked Data in the Virtuoso instance within PSNC infrastructure.
• Our next task was to show the integrated view over the original datasets. As target
datasets were particularly large (especially when considering connections with open
datasets), and the connections were not of equivalence (i.e., resources are related via
some properties (e.g., geometry) but they are not equivalent) it was decided to use
queries to access the integrated data as per need rather than using link discovery tools
like SILK or LIMES. Hence cross querying within the datasets were done in Virtuoso
SPARQL endpoint for some use cases to establish possible links between agricultural
and other related open datasets.
• To visualize and explore the Linked Data in a map different application/system
prototypes were created using the component called HSLayers NG as mentioned
earlier. (e.g. https://app.hslayers.org/project-databio/land/). One such visualization is
shown in Figure 84.
• As target datasets were particularly large (especially when considering connections
with open datasets), and the connections were not of equivalence (i.e., resources are
related via some properties (e.g., geometry) but they are not equivalent) it was
decided to use queries to access the integrated data as per need rather than using link
discovery tools like SILK or LIMES. Hence cross querying within the datasets were done
in Virtuoso SPARQL endpoint for some use cases to establish possible links between
agricultural and other related open datasets. The public instance of SILK is present in
http://silk.foodie-cloud.org/ .
• To visualize and explore the Linked Data in a map different application/system
prototypes were created using the component called HSLayers NG as mentioned
earlier. (e.g. https://app.hslayers.org/project-databio/land/). One such visualization is
shown below:
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Figure 84: Map visualisation prototype (HSLayer application) - http://app.hslayers.org/project-databio/land/
The resulting linked datasets are accessible via: https://www.foodie-cloud.org/sparql.
Preparation and execution of Trial 2
Trial 2 at Rostenice pilot farm was focused on the full area mapping of cereals and other crops
by satellite multispectral imaging and variable rate application of nitrogen fertilizers during
the 2019 vegetation period. The farm area increased from 8.300 to 10.087 ha during 2018 by
acquisition of smaller farm enterprise in neighbourhood.
The main fertilization strategy has changed during the project. Due to the frequent
occurrence of dry periods in the last two years (2018 and 2019), the monitoring of the current
crop status has gradually lost its importance and attention has been more focused on the
accuracy of delineation of management zones based on the EO data analysis. The reason is
simple, the current crop status observed by remote sensing does not have to reflect the
nutritional status during the dry period. Thus, the dosage of N is more dependent on the
expected yield than the diagnosis of plant nutritional status. Thus, main aim of the Trial 2 was
to evaluate variable rate application of nitrogen fertilizers based on the long-term analysis of
satellite multispectral imagery from free available data sources, such as Landsat and Sentinel-
2.
Trial 2 timeline
Timeline of Trial 2 follows the vegetation period of cereal crops in the 2019. Variable rate
application of nitrogen fertilizers was carried out during the spring (March 2019) in the form
of 1st top-dressing application for winter cereals and as the application before sowing of
spring cereals (maize, spring barley).
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Preparation for Trial 2
Preparation for Trial 2 included processing of EO imagery from the Landsat 8 and Sentinel-2
repository, both as the surface reflectance products with calculation of basic set of vegetation
indices as the next step.
For definition of yield productivity zones, 8-year time-series of Landsat imagery data was
processed with the results of relative crop variability. Final map is represented as percentage
of the yield to the mean value of each plot, later multiplied by expected yield [t.ha-1] as the
numeric variable for each field and crop species. Values of yield potential can be also
reclassified into three or five categories (zone maps) – high, middle and low-yielded areas.
Figure 85: Graphs of Sentinel-2 NDVI during the vegetation period 2019 for winter wheat (above) and spring barley (bellow) at locality Otnice (Rostenice farm). Low peaks indicate occurrence of clouds within the scene (Source: Sentinel-2, Level L1C, Google Earth Engine)
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Figure 86: Example of the output map products from yield potential zones classification from EO time-series analysis: classification into 5% classes (left), 5-zone map (middle) and 3-zone map (right). Blue/green areas indicate higher expected yield
Figure 87: Map of yield potential zones (5-zone map) updated for 2019 season from 8-year time-series imagery; for southern (left) and northern (right) part of Rostenice farm
Trial 2 execution
Variable rate application of fertilizers
Prescription maps for variable rate application of nitrogen fertilizers were prepared by simple
reclassification and values editing tools in GIS. The value of nitrogen rate was determined
based on the agronomist experience and knowledge of the site-specific production conditions
and crop variety needs. Final step was an export of prepared maps into shapefile/isoxml
format and upload into machinery board computers (mainly Trimble or Mueller Elektronik).
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Figure 88: Variable rate application of solid fertilizers by Twin Bin aplicator on Terragator
Figure 89: Variable rate application of liquid N fertilizers (DAM390) by 36m Horsch Leeb PT330 sprayer
Crop yield mapping
In 2019 were acquired yield maps by the combine harvester on the area over 3675 ha of grain
crops (winter wheat, spring/winter barley, oilseed rape) and 2786 ha of silage maize by forage
harvester. Data was later processed for outlier analysis and by spatial interpolation
techniques to obtain final crop yield map in absolute [t.ha-1] and relative [%] measure. Crop
yield maps are used for validation of yield potential maps estimated by EO imagery.
Statistical testing of crop yield maps from 2019 and regression analysis with set of Sentinel-2
vegetation indices are still in process. However, the results from recent years showed the
relationship between vegetation indices and yield values of crops. Correlation coefficients
varied among observed fields; closer relationship was discovered on the fields with higher
spatial variability.
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Figure 90: Crop yield maps from 2019 harvest
Figure 91: Graph with changes of correlation coefficients between winter wheat and set of Sentinel-2 vegetation indices during the vegetation period 2018. Most sensitive period was detected in Mai and June
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Figure 92: Graph of correlation coefficients between winter wheat yield maps and Sentinel-2 NDMI (2018/06/10) among observed fields. Highest correlation was detected on the fields with higher acreage and spatial heterogeneity
Trial 2 results
In Trial 2 was implemented a crop monitoring by Earth Observation tools in the pilot farm on
the farm area over 10.000 ha. The main area of interest was the introduction of variable rate
application of nitrogen fertilizers according to the assessment of nutritional status of crop
stands.
The main result of Trial 2 is the introduction of variable application of nitrogen fertilizers in
the pilot farm Rostěnice a.s. This was carried out on an area of about 3000 ha in the form of
a basic N application before sowing spring barley, maize and top-dressing N application during
the vegetation period of winter cereals. The main input layer is a yield potential map, which
is calculated from 8-year time series of satellite images (Landsat) and represents the
delimitation of management zones corresponding to the resulting land productivity.
Acquiring crop yield data in the form of yield maps allows to validate yield potential maps
from EO that have reached approximately 75% compliance with yield maps. Precise
quantification of the benefits of the applied procedures on the pilot farm is difficult because
there was no direct savings of applied fertilizers, but increased efficiency due to redistribution
of nitrogen doses with respect to expected yield. Although the total consumption of fertilizers
has not changed, it is precisely by targeted application according to yield levels that the
efficiency of fertilizer utilization can be expected somewhere around 8%.
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Components, datasets and pipelines
DataBio Component deployment status
Component code
and name
Purpose for pilot Deployment
status
Component
location
C09.12: OpenLink Virtuoso
Publishing he Czech farm and open data as Linked Data and allowing querying of the datasets via SPARQL endpoint.
operational PSNC infrastructures
C02.01 UWB/SensLog
Service, for the collection, processing and publication of sensor data.
testing Lesprojekt serves
C02.03 LESPRO/HSLayers,
Visualisation of data operational Lesprojekt servers
C02.06 LESPRO/Data model for PA
Integration of various farm data and data from other sources
operational Lesprojekt servers
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Data Assets
Data Type
Dataset Dataset original source
Dataset location Volume (GB)
Velocity (GB/year)
Sentinel-2 vegetation indices
Sentinel-2 L2A
ESA openhub repository
https://scihub.copernicus.eu/dhus/#/home
1500 GB 245 GB/year
Landsat vegetation indices
Landsat 5,8 Level 2 Surface Reflectance
USGS ESPA
https://espa.cr.usgs.gov/index/
300 GB 24 GB/year
Sensor data
Yield maps - shp point data
grain harvester
Lespro server 2,5 GB (2018)
2,5 GB/year
Czech farm RDF data
Farm oriented Linked Data (field and crops, field boundaries in a farm, Yield mass data for some fields) in N-triples format
Shape files provided by Czech partners to PSNC
Virtuoso server within PSNC infrastructure
~ 1.5 GB
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Czech Open RDF data
Linked Open Data (Czech LPIS, Soil maps, erosion zones, wate buffers) in N-triples format
Shape files provided by Czech partners to PSNC
Virtuoso server within PSNC infrastructure
~11 GB mostly static
DEM DMR4G DMR4G CUZK
arcgis online 0,1 GB
Exploitation and Evaluation of pilot results
Pilot exploitation based on results
The biggest success of the pilot Trial 2 is the successful introduction of variable application of
nitrogen fertilizers based on satellite monitoring into the real plant operation on the farm
fields. Although Rostěnice a.s. plays in its region a role of a pioneer in the use of precision
farming technologies, they have long been hesitant about choosing the right technology for
variable N fertilizer application. After the initial scepticism of the use of crop sensors in terms
of their demands on their operation, they finally decided on a variable application based on
delineation of the management zones from the yield potential maps and the strategy of
increasing the N dose in areas with higher expected yield. This strategy has proved to be a
promising option with regard to more arid farming conditions (and the absence of irrigation,
where the main yield limiting factor is the availability of soil moisture). Testing of VRA has
been started on the selected fields with spring barley (over 150 ha) in 2018. In this case, spring
barley for beer production was chosen as the most sensitive crop to the N application,
because of the difficulty of achieving malting quality in more arid conditions (sum of
precipitation from March till July 2018 at the level of 152 mm). Inadequate nutrition of plants
by nitrogen leads to significant yield reductions, while excessive N doses decrease the malting
quality of grain. During the last growing season (2019), a variable application of N fertilizers
on an area of more than 3,000 ha was launched. This included base N fertilizing before sowing
spring barley and maize and 1st N application in top-dressing of winter cereals (winter wheat,
winter barley). Beside the plan, testing of variable application of crop growth regulators in
spring barley by combination of yield potential zoning from EO time-series analysis and actual
crop status monitoring from Sentinel-2 imagery was also started. The results of this testing
will be available during winter 2019/2020.
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KPIs
KPI
short
nam
e
KPI
descripti
on
Goal
description
Base
value
Target
value
Measure
d value
Unit
of
value
Comment
EO
proc
essin
g
area
Area of
processe
d EO data
Covering the
maximum of
pilot farm
area
1500 8300 10000 ha
Zone
delin
eatio
n
accu
racy
Accuracy
of
manage
ment
zones
delineati
on by
field
survey
and yield
maps.
Estimate
d as the
deviation
to yield
zones.
Increase the
quality of
field zoning
50 75 75 %
Fertil
izers
use
effici
ency
Increase
of
fertilizers
use
efficiency
and farm
productivi
ty
Increase of
fertilizers use
efficiency
5 10 8 % Estimation
of fertilizer
usage
efficiency
was
influenced
by the
drought
occurrence
during the
vegetation
period 2018
and 2019
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11 Pilot 9 [B2.1] Machinery management Pilot overview
This pilot is focused mainly on collecting telematic data from tractors and other farm
machinery and analysis of these data in relation with other farm data.to analyse and compare
to other farm data. The main goal is to collect and integrate data and receive comparable
results. A challenge associated with this pilot is that a farm may have tractors and other
machinery from manufacturers that use different telematic solutions and data
ownership/sharing policies.
Summary of pilot before Trial 2
During Trial 1 the number of monitored Zetor tractors increased to 50. The datasets on
LESPRO’s servers contains current or historical data from 21 tractors of various brands and
models.
Figure 93: Tractor trajectory and work log
Unlike most other DataBio agricultural pilots that target a field, farm, or wider Territory in
Task 1.4, the pilot B2.1 collects mainly data from tractors wherever they are working, so farm
data are available only for part of the farms, where the tractors are in operation.
However, even in cases where data directly provided from the farm are missing, the data from
tractors can be combined and analysed at least in context with data on the farmer’s blocks
across the whole Czech Republic because the boundaries of farmer’s blocks are part of
publicly available LPIS (Land Parcel Identification System). More detailed farm information is
available only for some farms where tractors are used.
Analysis of data from Zetor tractors during Trial 1 and comparing them to data from other
tractors collected before DataBio project or during DataBio project led to several findings.
The technical solution of the data collection process from tractors of different brands and
models is the easier part. The greater challenge is to ensure the comparability of the
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information contained in the data for the purposes of various analyses, for example fuel
consumption in various parts of farmer’s blocks. In addition to GPS accuracy a key role is
played by the frequency of data collection, the interval between data transmissions, and data
processing between data acquisition and data transmission.
Some tractor models with some monitoring units are able to send only current values and do
not take into account the values between data transmission, others are able to measure
values more often and send aggregated values. In the first case, the results are rough
estimates, in the latter case they are values that are closer to reality. Although the data flows
through CAN bus and ISOBUS are based on standards, these standards include both
mandatory and customizable parts and implementation differs in different brand of tractors.
Although it is generally known that correction of GPS signals is required for the purpose of
automatic guidance of tractors, accuracy without corrections is sufficient for some types of
analysis. In this case, the frequency of data collection is important. Too long intervals between
position recording cause the trajectory to be very inaccurate, especially in places where the
tractor is turning. Setting the position record frequency is a compromise between the
trajectory accuracy and the amount of data transferred. According to LESPRO’s finding, it is
hard to set the ideal recording frequency for both the purpose of diagnostics and tractor
maintenance planning, as well as analgesics for precision farming and getting inputs for
economic analysis, if the user wants to optimize the amount of data transferred. Apart from
the fact that different variables are a subject of interest of interest in these cases, diagnostic
purposes do not require such frequent GPS position collecting as analysis for precision
farming and economic analysis. It is therefore appropriate to use different data collection
frequencies, depending on what services the customer wants to use.
PSNC took the initiative to perform an experimentation associated with the Pilot 9 [B2.1]
Machinery management where sensor data from the SensLog service (used by
FarmTelemeter service) has been transformed into Linked Data on the fly. SensLog performs
collection and processing of vital sensor data that served as the input for the transformation
and publication of sensor data as Linked Data. So, in this use case, data stays at the source
and only a virtual semantic layer was created on top of it to access it as Linked Data (RDF).
Preparation and execution of Trial 2
Trial 2 timeline
Most DataBio Agriculture Pilots were scheduled to begin in period from April to May 2019
and end in period from August to October 2019. Machinery management pilot sticks with this
schedule from the reporting point of view but process of data collecting continued even
between the trials as this trial is not directly dependant on growing season and it makes no
sense to interrupt collecting data between trials.
Preparation for Trial 2
As in the first trial Machinery management pilot has no spec trial site as the data from tractor
during trials are collected wherever the monitored tractors move. Tractors used in both Trials
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are owned by Zetor and collected data are being used mainly by the Testing and Development
Department of Zetor company.
Most of the Tractors are rented by farmers and Zetor Testing and Development Department
monitors these tractors in real-time operation during farm work using Zetor’s telemetry
solution, other tractors are operated in Zetor testing facilities. Farmers use these tractors for
their daily activities on the farm, Zetor uses telemetry to monitor the reliability of tractors
and their systems and to identify problems and plan maintenance. Consent to the collection
and processing of data is part of the tractor rental contract.
Figure 94: Zetor Major
50 tractors owned by Zetor company were involved in Trial 2. The models of Zator tractors
used in DataBio project involves Crystal 160, Crystal 170 HD, Forterra 140 CL, Forterra 140
HD, Forterra 150 HD, Forterra 140 HSX, Major CL, Major HS, Proxima CL 100, Proxima 110 GP,
Proxima 120 HS.
All of these tractors are equipped by monitoring units and telemetry service developed by
external supplier and adjusted to Zetor’s need according Zetor’s requests.
As one of Zetor’s Long Term goal is gradual development of services and adapting both
hardware and software to the needs of precision agriculture, during DataBio project new
functionalities have been added to Zetor telemetry before Trial 2. This extension of
functionality concerns mainly a basic information on the movement of tractors on LPIS land
blocks. Extending functionality in this direction is part of the services Zetor wants to offer to
their customers as native Zetor solution. Another new functionality includes extending
number of variables that can be exported from Zetor telemetry. These new possibilities of
export are important for viewing and analysing data from Zetor Tractors in third-party
systems.
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Zetor’s telemetry service allows to set frequency of data collection. During Trial 1 the interval
between collecting positions and values of additional variables was 10. Although it would be
beneficial to increase frequency of data collection and use interval 1 or 2 seconds, the interval
remained the same for Trial 2.
The reason is contract between Zetor and supplier of telemetry service where amount of
transferred data and used storage space affect the price of the service. As 10 seconds interval
of data collection is currently sufficient for main needs of Development and testing
Department, interval of data collection wasn’t adjusted for Trial 2.
The same telemetry service, that is used for purposes of Testing and Development
Department is offered as optional service to Zetor’s customers buying Zetor tractor, but
although those data would be valuable for the pilot from those Tractor’s aren’t part of
DataBio Machinery management trials due to data protection reasons. It was decided that
during Trial 2 additional monitoring units will be deployed on several Zetor tractors in parallel
with above mentioned monitoring units to test different ways of data collecting and
processing.
For this purpose, the need to involve third party partner was identified and ESTE Technology
was selected as new partner for the Trial 2. ESTE Technology is member of FederUnacoma
association who is partner in Machinery Management pilot since the beginning of DataBio
Project. Este technology will use their monitoring units and telemetry software to push
collected data to Lesprojekt FarmTelemetry through SensLog API.
As the one of the goals of the pilots is testing the possibility to use Zetor’s data in third party
systems, data gathered in Trial 2 through both ways are transferred and imported to
FarmTelemetry application used by LESPROJEKT.
Lesprojekt will use the data gathered in Trial 2 in relation with farm-related data from other
sources and test if the data can be used for the same purposes as data gathered from other
tractors outside DataBio project. As the farmers who uses tractors rented from Zetor
company aren’t member of DataBio projects, data about farms and fields will be limited only
to those which are publicly available, mainly as part of public LPIS dataset. Lesprojekt will also
use data from tractors by other manufacturers gathered outside DataBio project for
comparison of information contained in the data and evaluation of their usability farm related
analysis.
Trial 2 execution
Tractors involved in Trial 2 involved various models of Zetor Major, Proxima, Forterra and
Crystal. The variables recorded differed according to the specific configuration of the tractor.
Part of the variables is useful or potentially useful for agriculture related analysis in
FarmTelemetry, Part of the variables is important only for purposes Testing and Development
department of Zetor.
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Technically it is possible to import any type of data coming directly from tractors or other
telemetry system to FarmTelemetry as the list of recorded variables in FarmTelemetry can be
automatically extended according to the coming data.
If the data are supposed to be further processed and used in agriculture related analysis, it is
necessary to assign meaning to the individual variables. Lesprojekt focused mainly on the
variables, which are useful to agriculture related analysis and most of them have some
equaling with the same or similar meaning in data coming from other tractors brands and
models already recorded in FarmTelemetry.
From point of view of agriculture related analysis, the most important data for FarmTelemetry
are time stamp, speed, GPS coordinates, fuel tank level, fuel consumption (l/hour) or (km/l,
)engine RPM, engine load, RPM of PTO or status of PTO.
Other very useful information includes connected implement and data from connected
implement. At this stage, data about connected implement are not recorded in Zetor
Telemetry and can’t be imported to FarmTelemetry.
Two ways of import Zetor data were tested during Trial 2 on of them is using imports from
native Zetor telemetry solution, the other uses monitoring units and services provided by
ESTE.
Lesprojekt used subset of data from Zetor tractors to test various analysis related to tractor
work on fields (LPIS) block like daily activity log including fuel consumption, daily and monthly
field activities overview etc.
Including tractor data from new source in some cases requires modifications of existing
analysis according to the available variables, data quality and data frequency. In some cases,
these modifications involve only adjusting several parameters, sometimes it is necessary to
use different formulas or different algorithms for performing analysis based on data from
different tractors. One of the main points, where this analysis differs according to the data
source is fuel consumptions. Original fuel consumption related analysis in FarmTelemetry
were based mainly on (l/hour) variable, in case of Zetor tractors it is necessary to use fuel tank
level.
Trial 2 results
During Trial 2 data from 50 Zetor tractors were collected from winter to October 2019. The
data collecting and processing continues even after closing the Trial 2 formally.
Data collected during the trials can be displayed an analysed in both Zetor Telemetry and
FarmTelemetry.
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Figure 95: Daily tractor utilisation and trajectory in FarmTelemetry
In addition to the needs of Testing and Development department the analysis of gathered
data showed that the technological solutions are suitable for basic agriculture work related
analysis, but the parameters of the data collection process have some limits. One of the limits
is low frequency of data collection, does not allow to accurately depict the tractor trajectory.
The problem is particularly noticeable at the headland where the recorded trajectory shows
sharp spikes.
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Figure 96: Spikes caused by 10 seconds interval
The following figure shows the trajectories of another tractor with a data collection rate of 2
seconds. Of course, the result is also affected by other factors such as tractor speed and GPS
accuracy. Neither of these records provides data that is accurate enough to calculate, for
example, an application overlay, both trajectories are sufficient for creation of daily activity
log of tractor or commutation of statistics like time spend on each field, but the trajectory on
the second image provides a better overview. However, importantly, this limitation is due
only to input data and customers who use Zetor telemetry as a commercial service and have
priorities other than testing and development departments can increase frequency data
collection and get better results.
Figure 97: Data collection with 2 seconds interval
The second limitation is that calculation of Zetor tractors fuel consumption is based only on
the fuel level in the tank, not on instant consumption. Calculation results based solely on fuel
level in tank are sufficiently accurate when calculating over a longer time interval, but data
for a shorter time period may be affected by fluctuations caused by tractor movement and
terrain. The combination of both measurement methods is ideal for fuel consumption
calculations.
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Figure 98: Fluctuations in fuel tank measurement
Additional result of Trial 2 is that data imported to farm telemetry are available for publication
through linked data pipeline provided by PSNC.
Components, datasets and pipelines
DataBio component deployment status
Component code
and name
Purpose for pilot Deployment status Component
location
C02.01
UWB/SensLog,
Service, for the
collection,
processing and
publication of
sensor data.
Senslog is required
by
FarmTelemetryser
vice.
operational Lesprojekt
servers
C02.05
LESPRO/FarmTele
metry
Extension of
SensLog for
processing,
analysis and
publication of data
from mobile
sensor units.
Tractors are
considered to be a
operational Lesprojekt
servers
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mobile sensor
unit.
C02.03
LESPRO/HSLayers
Visualisation of
data from tractors
and other farm
data.
operational Lesprojekt
servers
D2RQ Server Transformation of
the Linked Data
from the mapping
file of SensLog
data and
publishing the
data on the fly
operational PSNC
infrastructur
es
C02.06
LESPRO/Data
model for PA
Linking data from
tracts with other
farm data.
operational Lesprojekt
servers
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Data Assets
Data Type Dataset Dataset original
source
Datase
t
locatio
n
Volume
(GB)
Velocity
(GB/year
)
Farm data LPIS Ministry of
Agriculture
http://eagri.cz
Lespro
jekt
servers
~4 GB ~4 GB
Machinery
data.
Tractors data
in
FarmTeleme
try
Collecting from
Tractors by
Wirelessinfo
and Lesprojekt
Lespro
jekt
servers
Depends
on what is
considered
as part of
datased.
Raw
positions +
other
variables
20 GB.
Basic data
Including
indexes
and
various
processed
data ~ 100
GB
Several
GB/year
Machinery
data
Original data
from Zetor
Tractors
Data collected
by Zetor
Server
s of
Zetor’s
third
party
service
provid
er and
Lespro
~ 1 GB
(raw data
optimised
for
transfers
from
monitoring
units)
~500 MB
/year
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jekt
servers
Sensor Data Original
sensor data
from
SensLog
Collection of
Sensor data by
Lesprojekt into
relational
Databases of
SensLog
D2RQ
server
within
PSNC
infrast
ructur
e
~ 10 MB ~10 MB
Exploitation and Evaluation of pilot results
Pilot exploitation based on results
There are several main directions for exploitation of pilot results. Zetor is going to continue
to use their telemetry for purposes of Testing and Development department. Tractor is a
complex mechanical product, which has to fulfil many mandatory safety, ecological, reliability
and technical standards. Development of new product – new tractor is usually process for
many years. Based on it is necessary to look for technologies, which could speed up this
process, make development process cheaper and much more efficient. Telemetry is very
helpful for this process as it can help to perform remote and in real time observation of
reliability tests and Remote and in real time observation of tractor CAN Bus communication,
tractor control unit’s analysis and other. Other part where telemetry helps is creation of long-
term library parameter which are used as an objective from real design work by new products.
Telemetry implemented to support development phases of new tractors can easily be
adapted for additional commercial usage.
One of the main users are farmers using the tractors. They have various requirements based
on the following factors:
• number of brands and models of tractors they use at their farm and telemetry systems
of other manufacturers
• The level of adoption of ICT for agriculture, farm management information systems
etc.
The following paragraphs focus on various functionalities of telemetry systems, their usability
for various groups of farmers or other users of DataBio Machinery Management pilot for
these types of functionalities.
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Displaying tractor’s position in real time
This is one of basic functionalities of most vehicle tracking system. Knowledge of the current
position of the tractor is useful mainly for security and for fleet management. Another use of
this functionality is the supervision of the work of tractor drivers. However, for supervision of
work, the knowledge of the current position of use is mainly used for the detection of
potential problems as movement of the tractor in places where it should not be at the
moment. For a more detailed analysis of the quality of work, the history of recorded positions
combined with other data from the tractor and external sources is more important. Knowing
the current position of tractors is useful information for all types of farmers, including those
with low ICT use, as it has low demands on knowledge of users and information systems and
data inputs from the side of farm management. Zetor telemetry has this functionality
covered. FarmTelemetry has support for this functionality but in case of Zetor tractors from
Machinery Management pilot, the data are not transferred to FarmTelemetry in real time.
The possibility of providing this information to a third-party system would depend on the
strategic decision of Zetor Management.
Tractor data recording and analysis of work on LPIS blocks
This functionality may include several different levels. Zetor telemetry supports displaying the
trajectory and calculating basic statistics on the time and fuel consumption on individual LPIS
blocks. This basic level requires minimum data impute from the side of farmer as boundaries
of LPIS blocks can be obtained from publicly available datasets. These results make it easier
for farmers to calculate the cost of specific work and the cost of a field or crop. Covering this
functionality in Zetor telemetry is a step on the path to development of additional services
related to precision agriculture.
Depending on the tractor manufacturer's telemetry system and the fragmentation of
information between systems, the limiting factor for farmers can be especially when Zetor
production is not focused on the most powerful tractors, and Zetor is often in the position of
the second tractor on the farm.
During the DataBio project, two ways to import Zetor telemetry data into a third-party system
(FarmTelemetry) and to perform a similar field work related analysis were tested. This is a
new opportunity for Zetor management to consider opening telemetry data to third party
systems and give tractor owners more freedom to use telemetry data from their tractors in
any way they need.
However, despite this possibility, further development of Zetor's native telemetry remains
one of the strategic priorities for Zetor management and the experience of the DataBio
project will be useful for this goal.
For LESPROJET the machinery Management Pilot provided opportunity to access tractor data from new source and extend functionality of FarmTememetry to be able to receive data from new sources and used them in field works related analysis
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Additional benefit is tractor data ready to be published through linked data pipeline provided by PSNC which allows future synergies with linked data activities carried out in other pilots, mainly B1.4
Other users
In addition to the main telemetry users, which are tractor manufacturers, farmers and
advisors providing services to farmers, banks are another user. This applies in cases where
banks provide leasing products and require monitoring of the tractor, which is the bank's
property at the time of the lease. Now they are using native telemetry solutions provided by
Zetor, but it is important to take into account the possibility that banks may later begin to
require direct access to data and use their own tools.
KPIs
KPI
short
nam
e
KPI
descripti
on
Goal
description
Base
value
Target
value
Measur
ed value
Unit of
value
Comment
Tract
ors
total
s
Numbers
of tractors
and
agricultur
al
machinery
using
DataBio
solutions.
Include as
much
tractors as
possible
0 30 50 (71) numbe
r
Data from
21
tractors as
historical
data for
compariso
n. Data
from 50
Zetor
tractors
gathered
during
databio.
Number
of various
tractor
brand/mo
dels
tested.
Include data
from
multiple
tractor
models
NA NA 11
numbe
r
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Amount of
collected
data
NA Na ~ 1 GB
GB Raw data
optimised
for
transfers
from
monitorin
g units.
Amount of
Data
including
various
precompu
tations
and
indexes
can be ~
10 times
bigger
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12 Pilot 10 [C1.1] Insurance (Greece) Pilot overview
The main focus of the pilot is to evaluate a set of tools and services dedicated for the
agriculture insurance market that aims to eliminate the need for on-the-spot checks for
damage assessment and promote rapid payouts. The pilot concentrates on fusing
heterogeneous data (EO data, field data) for the assessment of damages at field level. NP will
lead the activities for the execution of the full lifecycle of the pilot with the technical support
of FRAUNHOFER and CSEM. Moreover, a major Greek insurance company, INTERAMERICAN,
is actively engaged in the pilot activities, bringing critical insights and its long-standing
expertise into fine-tuning and shaping the technological tools to be offered to the agriculture
insurance market. The methodology of the pilot activities involves the integration of high-
power computing and EO-based geospatial data analytics for conducting damage assessment
with data from IoT agro-climate stations for field-level condition monitoring. The convergence
of the aforementioned technologies in a single dedicated framework is expected to deal
effectively with insurance market demands which require a smooth transition from
traditional insurance policies (expensive, require human experts for damage assessment) to
more flexible index-based insurances. Index-based insurance provides transparency and
reduces bureaucracy since it is based on objective predefined thresholds. It has low
operational costs requiring minimal human intervention. On the top of that, this new type of
insurance can eliminate field loss assessment, adverse selection and moral hazards since the
whole process is fully automated, meaning that the point where the pay-out starts (trigger)
and the point where the maximum pay-out is reached (exit) are based on a prespecified fixed
model per crop. Key stakeholders of the pilot are the farmers, which wish to insure their crops
against weather-related systemic perils (e.g. floods, high/low temperatures, and drought) and
INTERAMERICAN, as a major Greek insurance company, with increased interest in agricultural
insurance products. The pilot activities are performed at Northern Greece targeting at high-
impact annual crops (e.g. tomato, maize, cotton, wheat etc.).
Summary of pilot before Trial 2
The pilot has completed the first round of trials during Trial 1 on annual crops (e.g. tomato,
maize, cotton) in two regions, namely Evros and Thessaly with significant economic footprint
on the Greek agri-food sector. The incidents that were evaluated (floods and heatwaves) fall
under the definition of the climate-related systemic perils. The pilot effectively demonstrated
how Big Data enabled technologies and services dedicated for the agriculture insurance
market can eliminate the need for on-the-spot checks for damage assessment and promote
rapid payouts. Important insights have been gained from Trial 1 and shaped the execution of
Trial 2. The role of field-level data has been revealed as their collection and monitoring is
important in order to determine if critical/disastrous conditions are present (heat waves,
excessive rains and high winds). Field-level data can be seen as the “starting point” of the
damage assessment methodology, followed within the pilot. Moreover, regional statistics
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deriving from this data can serve as a baseline for the agri-climate underwriting processes
followed by the insurance companies who design new agricultural insurance products.
Preparation and execution of Trial 2
Trial 2 timeline
The following roadmap applies for the pilot activities
Figure 99: Pilot timeline
Preparation for Trial 2
The following work was conducted by NP, as part of the preparatory work for Trial 2:
• As the requirements in terms of sensors deployed for in-the-field usage differ between
pilot sites, it became obvious that several adaptations were necessary in respect to
C13.03 and the way data was represented for both cloud-based storing and Gaiatron
station configuration. More specifically, all relational and EAV (Entity-Attribute-Value)
data representations were adapted to more flexible and scalable JSON format that
performs better in a dynamic IoT measuring environment. The latter is widely
acknowledged as JSON has become gradually the standard format for collecting and
storing semi-structured datasets that originate from IoT devices. The adaptation to a
JSON format for modelling IoT data streams allows the further processing, parsing,
integration and sharing of data collections in support of system interoperability
though the adaptation on well-established and favoured linked-data approaches
(JSON-LD).
• The work initiated as part of C13.02 GAIABus DataSmart Machine Learning
Subcomponent evolved further on Trial 2 by using statistical methods for EO-based
crop modelling. Lessons-learnt from previous research activities validated the
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applicability of statistical solutions in agro-insurance use cases14. More specifically,
crop type and area tailored crop models have been created for the whole Greek arable
area making use of NDVI measurements that have proven to be suitable for assessing
plant health. In total, for each one of the 55 Sentinel-2 tiles that cover the whole Greek
arable land, 7 major arable crops for the local agri-food sector were modelled (as
suggested by INTERAMERICAN) and namely: wheat, maize, maize silage, potato,
tomato, cotton and rice (55x7=385 models in total). The models were developed
exploiting multi-year NDVI measurements from the available last three (3) cultivating
periods and instead of using sample statistics (few objects of interest but many
observations referring to them), population statistic methods (large number of objects
of interest but with few observations referring to them) were employed instead in
order to identify NDVI-anomalies. As sound insurance models are typically created
using large multi-year historical records (~30 years), this approach is ideal for deriving
robust estimates for setting anomaly thresholds (exploiting the space-time cube to
have enough degrees of freedom). The goal is to detect deviations in NDVI
measurements in respect to what is considered normal crop health behavior for a
specific time instance. Thereby, each crop model consists of 36 NDVI probability
distributions that refer to all decads of the year. By adjusting these high and low
thresholds (part of the strategy of the insurance company), it is evident that
measurements found at the distribution extremes can be spotted and flagged as
anomalies (Figure 100). Typically, insurance companies are looking for negative
anomalies (below 15%) that provide strong indications of a disastrous incident.
Figure 100: Crop NDVI probability distribution referring to a decad of the year (Wheat-Larisa region-2nd decad of February). Anomalies can be found at the distribution extremes
14 de Bie, C. A. J. M., B. H. P. Maathuis, and A. Vrieling. "Improved drought detection to support crop insurance models: powerpoint." Proba-V Symposium 2018. 2018.
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The following figures graphically depict three different crop models created using the
aforementioned procedure:
Figure 101: Cotton model in Komotini region (T35TLF tile, Maize model in Evros region (T35TMF tile) and Wheat model in Larisa region (T34SFJ tile) by decad (horizontal axis)
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In the previous figures, Light green threshold indicates lower 15% extremes while dark green
threshold indicates upper 85% extremes of the probability distribution. Red line is presenting
a single parcel status for the whole 2018 with its NDVI measurements staying within “normal”
ranges for the critical cultivating periods.
During the preparatory phase of Trial 2, CSEM continued on improving the accuracy of its
C31.01 Neural Network Suite for specific crop classes that can be considered a baseline for
future crop modelling activities. As a first step, a structured method of digitizing expert
knowledge in a data-driven architecture was offered. A pipeline was developed significantly
reducing the complexity of creating models by removing the need of hand-crafted filtering,
making it a cost-effective option for bringing neural network models to the market. It was
identified that is was important to verify the reliability of the data with minimum supervision
and then, use the clean data to train the network for the classification problem at hand. All
the efforts, led to an overall accuracy in terms of classification over 92% for Maize, Wheat
and Legumes. Further investigation on particular taxonomical varieties found that training a
crop model with one variety and testing with other varieties performed well, apart from the
crop type Legumes, which shows a large intra-class variability. This aspect of creating a model
with only one variety has the potential to simplify the creation of models in the future. As this
methodology is pixel-based it can be derived that in the aftermath of a disastrous effect, low
classification probabilities for the monitored crop type could be a strong indication of disaster
and could be used in damage assessment approaches.
The preparatory work by FRAUNHOFER for Trial 2 concentrated on the development of an
adaptive analytic platform for geospatial data that allows the integration of services on top
of it. For this purpose, a reference architecture has been drafted that allows to orchestrate
different data sources, processing services and UI components to fulfil the needs of a specific
use-case. What was identified during the preparatory stage of Trial 2 is that this work has a
horizontal impact and provides solutions for multiple use cases scenarios spanning from
Smart Farming, to CAP Support and Agri-insurance.
The preparatory work conducted by FRAUNHOFER for Trial 2 concentrated on the discussion
how existing analytic services could be integrated into a web-based analytic-platform with
ease. The starting point for this was provided by the solution developed during Trial 1. During
this discussion, a variety of ideas were developed how different services could be integrated
into a single platform, which is also able to cope with multiple data sources, to fulfil the needs
of specific use-cases. One of the major challenges of such efforts is to reduce the complexity
of integration. Principles of modern architecture styles such as Self-contained Systems (SCS)
(https://scs-architecture.org) or Microservices were considered to promote the separation
into independent components. Each component consists of capabilities to access data,
process or analyse it and consequently visualize the result. The integration itself must be done
at the UI-Layer. Following this approach provides more flexibility and eventually allows to
think about a platform which enables the users to build views for custom analytics tasks
composed by a variety of components. The horizontal impact of this stage can provide
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solutions for multiple scenarios spanning from Smart Farming, to CAP support and Agri-
Insurance.
The implementation of Trial 2 focuses primary on the integration of external services. In this
scenario a web-application was developed to enable professional users - to do crop type
classification on demand using latest or historic satellite images. A variety of visual analytic
tools are included to allow efficient exploration of available data. The functional capabilities
for the purpose of classification are offered by external services which in turn exploit methods
from the domain of machine learning (ML). The integration of services and data sources is
done using well-defined RESTful interfaces.
Trial 2 execution
During Trial 2 the following actions have been performed by the partners involved in the pilot
activities:
By M26, the DataBio platform v2 for the pilot is fully operational and involves offering to the
insurance company a set of tools and services for: a) damage assessment targeting towards a
faster and more objective claims monitoring approach just after the disaster (scenario 1), b)
the adverse selection problem. Through the use of high quality data, it will be possible to
identify the underlying risks associated with a given agricultural parcel, thus, supporting the
everyday work of an underwriter (scenario 2), c) large scale insurance product/risk
monitoring, that will allow the insurer to assess/monitor the risk at which the insurance
company is exposed to from a higher level (scenario 3).
The effectiveness of the methodology was tested against a flooding event (11/7/2019) in
Komotini that affected cotton farmers in the region and led to significant crop losses (Figure
102).
Figure 102: Aftermath of the floods in Komotini region (11/7/2019)
Initially, Gaiatron measurements confirmed that flooding conditions were present at the area
as a result of increased volumes of rainfalls (Figure 103). This proves that the region might
have been affected by the systemic risk and should be more thoroughly examined.
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Figure 103: Rainfall volume (mm) in the Komotini region
This triggered an EO-based crop condition monitoring approach that captures the impact of
the peril to crop’s health. After only 2 weeks the approach identified statistically significant
differences compared to the respective crop model that indicate damages at field level (Figure
104). This validates the initial hypothesis that floods were responsible for severely affecting
the region’s crop health and consequently proves that the established methodology can be a
powerful tool for early identification of potentially affected/damaged parcels, crop types and
areas (as described within scenario 1). The findings have been presented both to the
insurance company and the farmers in order to show how these technologies can bridge the
gap among the farming and the insurance world.
Figure 104: Parcel monitoring at Komotini region (cotton) showing negative anomaly (deviation) for two consecutive decads just after the disastrous incident
By mapping the outcome of the followed damage assessment procedures on top of a map, it
is evident that high-level assumptions can be made. This involves the risk at which the
insurance company is exposed to (scenario 3) and prioritizing the work that needs to be
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conducted by field damage evaluators (until now this process is not data-driven) that are
advised to begin with parcels exhibiting higher damage estimates and steadily move to those
with lower ones.
Figure 105: High-level overview of the affected area, color coded with the output of the followed damage assessment procedures
Finally, the exploitation of the wealth of agro-meteorological data (Gaiatron stations, EO
meteorological open data) also leads to the provision of underwriting services (scenario 2)
that provide critical statistical insights for better shaping agro-insurance products (Figure
106).
Figure 106: Risk analysis tool that measures the frequency of presence of extreme weather conditions (against heat-waves, frosts, or windstorms) as defined by ELGA15
By M28, a preliminary architecture for FRAUNHOFER’s analytics platform has been drafted.
The platform was the main discussion topic during the M28 DataBio Thessaloniki Codecamp,
hosted in NEUROPUBLIC’s N. Greece offices with the participation of other DataBio partners
15 http://www.elga.gr/organismos/thesmiko-plaisio/52-thesmiko-plaisio/nomoi/70-2010-04-28-08-48-39
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involved in the WP1 pilots led by NEUROPUBLIC. Furthermore the generalization and simple
adaption to other scenarios was discussed intensively.
By M32, a first instance of the aforementioned analytics platform has been finalized and
deployed. The use of ML services is available providing a proof of concept for its use in agri-
insurance scenarios (e.g. scenario 1 and 3). FRAUNHOFER was responsible for the
development of the UI, integrating map, pixel heat maps from the different classifiers and
information visualization capabilities
A CSEM developed system for the management of Machine Learning models was used to
facilitate the simple and retraceable management of models. RESTful services, combined with
security features in the form of JWT tokens and encryption with HTTPS, were implemented
and integrated into service. The service has also been containerized to allow simple
deployment. This service enables the communication with the FRAUNHOFER’s component
GeoRocket and UI for the on-demand classification, in both pixel and parcel levels, of crop
types.
Figure 107: FRAUNHOFER's UI screenshot colour coding different crop types
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Figure 108: FRAUNHOFER's UI screenshot that integrates CSEM’s classification results into pixel heat maps
By M34, the final KPI measurements are collected. More specifically, with regular discussions
with the farmers and INTERAMERICAN, final KPI measurements and feedback was collected
and can be found in Section 12.5.2.
Trial 2 results
In Trial 2, the applied technologies and pipelines got even more mature and reached their
expected TRL. INTERAMERICAN continued (for a second year) to benefit from agri-insurance
tools and services that perform EO-based damage assessment at parcel level and target
towards evolving to next-generation index-based insurance solutions. The pilot results clearly
show that data-driven services can facilitate the work of the insurance companies, offering
tools that were previously unavailable and were responsible for severe bottlenecks in their
day-to-day activities (e.g. long wait for ELGA’s official reports, dependence on the human
factor, difficulties in prioritizing work after receiving several compensation claims). However,
there is still room for methodological improvements. Specifically, more effort should be
placed on validating negative predictions in order to capture the true accuracy of results. Data
abundancy holds the key in delivering even more precise solutions and address issues relevant
to the multi-parametric nature of the problem as different climate-related perils affect
dissimilarly different crop types within their various phenological stages.
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Components, datasets and pipelines
DataBio component deployment status
Component code
and name
Purpose for pilot Deployme
nt status
Compone
nt
location
C13.01 Neurocode
(NP)
Neurocode allows the creation of
the main pilot UIs in order to be
used by the end-users (insurance
company, farmers) and offering
insights regarding weather-related
perils
deployed NP Servers
C13.02 GAIABus
DataSmart Machine
Learning
Subcomponent (NP)
Supports EO data preparation and
handling functionalities
Supports multi-temporal object-
based monitoring and modelling
for damage assessment
deployed NP Servers
C13.03 GAIABus
DataSmart Real-
time streaming
Subcomponent (NP)
Real-time data stream monitoring
for NP’s Gaiatrons Infrastructure
installed in all pilot sites
Real-time validation of data
Real-time parsing and cross-
checking
deployed NP Servers
C31.01 Neural
Network Suite
(CSEM)
Machine learning crop
identification system to be used
for the detection of crop
discrepancies that might derive
from reported weather-related
catastrophic events
deployed CSEM’s
Servers
C04.02 – C04.04
Georocket,
Geotoolbox,
SmartVis3D
(Fraunhofer)
Back-end system for Big Data
preparation, handling fast
querying and spatial aggregations
(data courtesy of NP)
Front-end application for interactive
data visualization and analytics
deployed Fraunhofe
r Servers
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Data Assets
Data Type Dataset Dataset
original
source
Dataset
location
Volu
me
(GB)
Velocity
(GB/year)
Sensor
measurem
ents
(numerical
data) and
metadata
(timestamp
s, sensor id,
etc.)
Gaiasense
field. Dataset
composed of
measurement
s from NP’s
telemetric IoT
agro-climate
stations called
Gaiatrons for
the whole
Greek area.
NEUROPUBL
IC
GAIA
Cloud
(NP’s
servers)
Sever
al
GBs
Configurable
collection and
transmission
rates for all
Gaiatrons. >200
Gaiatrons fully
operational at
several
agricultural areas
of Greece
collecting >
30MBs of data per
year each with
current
configuration
(measurements
every 10 minutes)
EO
products in
raster
format and
metadata
Dataset
comprised of
remote
sensing data
from the
Sentinel-2
optical
products (55
tiles for the
whole area of
Greece)
ESA
(Copernicus
Data)
GAIA
Cloud
(NP’s
servers)
>550
00
>18800
Parcel
Geometries
(WKT),
alphanume
ric parcel-
Dataset
comprised of
agricultural
parcel
positions
NEUROPUBL
IC
GAIA
Cloud
(NP’s
servers)
Sever
al
GBs
1 GB/year
The update
frequency
depends on the
velocity of the
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related
data and
metadata
(e.g.
timestamps
)
expressed in
vectors along
with several
attributes and
extracted
multi-
temporal
vegetation
indices
associated
with them.
incoming EO data
streams and the
assignment of
vegetation indices
statistics to each
parcel. Currently,
new Sentinel-2
products are
available every 5
days
approximately
and the dataset is
updated in
regular intervals
Exploitation and Evaluation of pilot results
Pilot exploitation based on results
In the context of DataBio, NP has initiated a close collaboration with INTERAMERICAN
(https://www.interamerican.gr/), a major Greek insurance company with a clear target in
evolving its agri-insurance products. This collaboration is expected to continue in the next
years as part of another high-profile research project, H2020 e-shape (https://e-shape.eu/),
where NP is a key partner in S6P4 “Resilient and Sustainable ecosystems including Agriculture
and food” and INTERAMERICAN the pilot’s co-designer. What is also being investigated is the
possibility to offer the agri-insurance services of INTERAMERICAN alongside with the Smart
farming ones of NP as part of a joint exploitation plan (and vice versa, i.e. Smart farming
services alongside agri-Insurance ones). This will allow both companies to widen their market
share.
From an implementation point of view, the quality of the provided services of NEUROPUBLIC
greatly benefited from the collaboration with leading technological partners like CSEM and
Fraunhofer, that specialize in the analysis of Big Data. Moreover, feedback from the end users
and lessons-learnt from DataBio’s pilot execution significantly fine-tuned and will continue to
shape the suite of dedicated tools and services, thus, facilitating their penetration in the agri-
insurance sector.
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KPIs
KPI
short
nam
e
KPI
descripti
on
Goal
descriptio
n
Base
value
Target
value
Measur
ed value
Unit
of
value
Comment
C1.1
_1
Accuracy
in
damage
assessme
nt
No
prior
inform
ation
availab
le
>80 95%
precisio
n
% Results are
available in
real-world
data, capturing
disasters
resulting from
extreme
weather
events (July
2019 -
Komotini
region - Cotton
cultivation
affected by
floods). As our
first priority
was to notify
and assess the
most-affected
parcels,
validation was
focus on
positive
predictions.
Precision
reached ~95%
effectively
showing that
data-driven
solutions can
significantly
prioritize and
reduce the
work required
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by an expert
evaluator.
C1.1
_2
Decrease
in the
required
time for
conducti
ng an
assessme
nt
Severa
l
month
s
Severa
l days
Two
weeks
approxi
mately
Days,
week
s,
mont
hs
This KPI
depends on
the availability
of reliable EO-
data in the
post-disaster
period. Cloud
presence or
absence plays
a critical role in
defining the
required time
for the
assessment.
We usually
need at least 2
post-disaster
EO-based
measurements
to reach
reliable
conclusions
and based on
Sentinel2
measuring
resolution, this
happens
approximately
within 2
weeks.
C1.1
_3
Number
of crop
types
covered
Initiall
y no
crops
were
being
covere
d by
7 7 crop
types
(based
on
specific
require
ments
plain
num
ber
7 major annual
crop types
were modelled
as suggested
by the
insurance
company for
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the
system
from
the
insuranc
e
compan
y) for all
55 tiles
coverin
g
Greece.
the whole
Greek arable
area (55tiles x
7 crops = 385
models in total
created) and
namely:
cotton, rice,
maize, maize
silage, tomato,
corn, potato.
In addition,
continuous
NDVI
monitoring
(measuring
NDVI
fluctuations
before and
after a
disastrous
incident) can
be actually
applied to any
crop type to
assess
damages at
field level
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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13 Pilot 11 [C1.2] Farm Weather Insurance Assessment Pilot overview
The objective of proposed pilot is the provision and assessment on a test area of services for
agriculture insurance market, based on the usage of Copernicus satellite data series, also
integrated with meteorological data, and other ground available data.
Among the needs of the insurances operating in agriculture, one of the most promising in
terms of fulfilment with Earth Observation data is the evaluation of risk assessment and
damages estimation down to parcel level.
For the risk assessment phase, the integrated usage of historical meteorological series and
satellite derived indices, supported by proper modelling, will allow to tune EO based products
in support to the risk estimation phase.
For damage assessment, the operational adoption of remotely sensed data based services
will allow optimization and tuning of new insurance products based on objective parameters,
such as maps and indices, derived from EO data and allowing a strong reduction of ground
surveys, with positive impact on insurances costs and reduction of premium to be paid by the
farmers.
In the initial stage of the pilot activities, a set of services has been planned, including:
1. Historical medium resolution Risk Map: historical risk maps, based on long time series of vegetation indices estimated form medium resolution satellite images (number of critical events for each area).
2. Field crop growth vs. similar crop (inter-field anomalies): Indicator on crop behaviour (average, worst, better) during current season comparing the single parcel behaviour and the average in the area.
3. Intra-field Anomalies: information about single parcel situation to detect the growth homogeneity and evidencing irregular areas in the parcel.
4. Correlation among weather historical data and critical events: specific indexes supporting the introduction of parametric insurance products, obtained by using machine learning methods that consider, as inputs:
○ meteorological relevant data
○ spectral specific indexes
○ field characteristic (e.g. soil type)
○ loss data from Insurance
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Summary of pilot before Trial 2
The services that have been set up in the Trial 1 are briefly described here after and first
results are presented.
Historical medium resolution Risk Map: The scope of the service is to provide historical risk
maps, based on long time series of vegetation indices estimated form medium resolution
satellite images providing, as output, a risk maps per crop (number of critical events for each
area).
The historical risk map refers to the occurrence of “damage” in the past. The map is based on
an index derived from time series of low-medium resolution satellite images. The index is
assumed to be correlated with crop yield.
“Damages” are mapped for each year in the time series by calculating on pixel basis the
difference between the actual index value and the long-term average. When the difference
exceeds a certain threshold, we assume there is damage. Ideally, the damage threshold is
defined based on reference data such as actual losses reported on the field. Geo-localized
crop loss data will be made available by the insurance company for the period 2012-2018 but
have not been received yet.
Figure 109: Map classifying the Netherlands territory in terms of number of years with damages
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Weather based risk map: A weather-based risk map is going to complement the historical risk
map calculated by VITO to detect the occurrence of “damages” in the past. Such damages are
in fact not explicitly correlated to weather events. The risk map is intended to show the
occurrence of extreme weather events in the past. It is then going to show a reliable
correlation between damages occurred to the crops and extreme weather events, heavy rains
in particular, to better define certain damage patterns or to further zoom in on areas with a
high damage frequency. At the end, 8 different risk maps are expected: 1 per threshold per
year. The risk map will be available as a raster image, in geotiff format. Moreover, starting
from the list of dates related to damage claims and provided by the insurance companies for
the years 2015-2018, the extraction of precipitation values (with the respective location
coordinates) has been performed, in order to find further locations (in addition to those
provided by the insurance company) where heavy rain events have occurred (Figure 105).
mm
Figure 110: Map of precipitation extracted from KNMI dataset on date 30/08/2015. Yellow points: locations provided by the insurance company – Blue points: further locations with 24-hours precipitation values above the 50 mm threshold
Finding new locations showing heavy rain events should help in finding changes in the
vegetation index. Over the coming trial, further meteo-climate variables could be taken into
account, such as temperature.
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Field crop growth vs. similar crop (inter-field analysis): The scope of the service is to
represent the status of the crop during the current season and to use it, in case of critical
weather events (flood, drought), to provide evidence that the potential damages are really
depending on the event or that the parcel was already in a critical situation in terms of
production capacity. The output of the service aims to provide and indicator on crop
behaviour (average, worst, better) during current season.
Starting from a shapefile grouping same crop fields in the area of interest, the developed tool
applies an inner buffer to each parcel and extracts the temporal profiles. Figure 106 show
some results produced by the analysis. We tested the process on winter wheat, onions and
potatoes considering S2 data from 2018-01-01 to 2018-11-15. In particular, account areas
affected by drought and frost have been taken in account and the results reveals significant
differences between temporal profiles of parcels impacted, with a high level of anomaly
(assigned by the tool), and parcels not impacted with a “normal” behaviour.
Intra-field Anomalies: The scope of the service is to analyse single parcel situation to detect
the growth homogeneity and evidencing irregular areas in the parcel, providing an indicator
of field anomalies. The vegetation variability within a parcel is mainly due to soil
characteristics such as texture and depth with consequences on water consumption and
irregular growth but it is also affected by extreme weather events (e.g. drought, excess of
rain, frost and heat). Starting from the temporal spectral profile of a parcel, the developed
tool identifies the period of maximum growth of the crop (if the parcel is the cultivated) and
calculates mean and deviation that are effective instruments for detecting anomalies.
Figure 111: Intra-field analysis based on NDVI spectral index with S2A and S2B data (tile T31UET - year 2018)
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Preparation and execution of Trial 2
Trial 2 timeline
Preparation for Trial 2
The first part of preparatory work conducted by 3-Geos has been focused on collecting and
processing both optical and SAR data over the Netherlands.
e-GEOS has implemented two pipelines consisting of several pre-processing steps performed
directly both on Sentinel-2 data and on Sentinel-1 data.
Here a brief description of the main steps:
● Sentinel-2 pipeline:
○ Automated product downloading and archiving
○ Pre-processing: atmospheric correction and cloud, snow and shadow masking
○ Vegetation index extraction (NDVI)
● Sentinel-1 pipeline:
○ Automated product downloading and archiving
○ Coherences and Amplitudes in VV/VH polarization
e-GEOS has collected about 1 year of both Sentinel-2 and Sentinel-1 data. A total of 10
Sentinel-2 tiles has been processed (Figure 112) and 3 relative orbits of Sentinel-1 has been
considered for generating amplitudes and coherences.
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Figure 112: Sentinel-2 tiles over the Netherlands
The second part of the preparatory work has been focused instead on extracting parcels’
statistics starting from GSAA data.
e-GEOS has developed a tool for extracting maximum, minimum, standard deviation and
count of pixels for each parcel (expressed by a polygon geometry) and for each satellite
acquisition by applying also an inner buffer to mitigate border effects.
Potato has been selected as crop of interest and we focused our analysis, in particular, on 5
types of potatoes:
• Consumption
• NAK, seedTBM, seed
• Starch
• AM, disinfestation
In Figure 113 an overview of the spatial distribution of potatoes (based on type) in the
Netherlands for reference year 2017 is presented.
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Figure 113: Spatial distribution of potato fields with respect to variety for year 2017
In the Figure 114 the count of samples per type are presented.
Figure 114: Count of samples per type of potatoes
Activities related to interfacing the Insurance Final User (NB Advies):
● Extraction of potato fields from LPIS on 5 types of potatoes:
○ Consumption
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○ NAK, seed
○ TBM, seed
○ Starch
○ AM, disinfestation
● Extraction of soil type for each parcel based on BOFEK2012 (Figure 115).
Figure 115: Soil type map
The preparatory work has been finalized by MEEO, to extract the following dataset for each
potato parcel:
• Precipitation (24H) from local weather stations (Figure 116)
• Evapotranspiration from EO Data Service MEA (Figure 117)
• Land Surface Temperature from EO Data Service MEA (Figure 117)
• Soil Moisture from EO Data Service MEA (Figure 117)
Figure 116: Meteo climate data from local weather stations
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Figure 117: Data from EO Data Service MEA
Figure 118 presents an example of temperature profile related to a potato parcel.
Figure 118: Temperature profile (parcel number 1971186)
Data analysis focused on the possible application of machine learning techniques in order to
overcome the lack of data from insurances (EXUS).
Trial 2 execution
The activities and the services that have been set up in the Trial 2 are briefly described here
after:
Weather risk map
A weather-based risk map is intended to show the occurrence of extreme weather events,
heavy rains in particular, in order to identify areas with possible high damage frequency. Four
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risk maps per year (from 2016 to 2018) have been created according to the following
thresholds indicated by insurance companies:
• 50/71 mm in 24h (depending on the agreement between farmers and insurance
company)
• 84 mm in 48 h
• 100 mm in 96 h
50 mm in 24h risk map for year 2016 71 mm in 24h risk map for year 2016
84 mm in 48h risk map for year 2016 100 mm in 96h risk map for year 2016
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50 mm in 24h risk map for year 2017 71 mm in 24h risk map for year 2017
84 mm in 48h risk map for year 2017 100 mm in 96h risk map for year 2017
50 mm in 24h risk map for year 2018 71 mm in 24h risk map for year 2018
84 mm in 48h risk map for year 2018 100 mm in 96h risk map for year 2018
Figure 119: 2016-2018 risk maps (split across pages)
Detection of parcels with anomalous behaviours and identification of more influencing
parameters
Trying to identify the parameters (weather or soil related) with the dominant impact on the
crop yield such as Normalized Difference Vegetation Index (NDVI) measurements the
following approach was first considered:
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For the 2017 dataset we went through the following steps for each one of the crop types
(potato):
1) Split parcels into two datasets.
2) Use the first part of the dataset for the clustering and create groups using satellite,
meteorological measurements and soil characteristics aggregated on the level of one
or two months considering a full growing season from March to October.
March – April Avg values for satellite
measurements
Avg values for meteo
measurements
May - June Avg values for satellite
measurements
Avg values for meteo
measurements
July – August Avg values for satellite
measurements
Avg values for meteo
measurements
Sep - Oct Avg values for satellite
measurements
Avg values for meteo
measurements
Soil characteristics of the parcel
Coordinates of the parcel
3) Characterize / label each group based on the NDVI values of their parcels.
After these steps we would have liked to continue to the prediction and feature selection
and use the second part of the dataset in order to apply the following procedure:
1) For each parcel try to identify in which cluster / group belongs considering its
measurements from March to October.
2) After selecting the group it belongs, use the prediction model that have been trained
in the measurements of the parcels that belong in the same cluster and predict NDVI
values.
Due to the limited number of usable measurements for the different parcels for the half of
the dataset we could not apply the prediction and feature selection per cluster. For that
reason, we used the full dataset of 2017 considering SAR and meteorological measurements
(such as precipitation, cumulative precipitation, temperature and cumulative temperature)
and soil characteristics for the prediction of NDVI values after 14 days or any other preferable
time window, e.g.: use the SAR and meteorological measurements for the 30/06/2017 and
predict NDVI value for 14/07/2017. And try to identify which are the dominant parameters
that affect the growing of the parcels for each crop type. For the prediction and feature
importance we use random forests. The higher the value of the importance for a feature the
stronger the correlation with the NDVI value. For the dataset of 2017 considering SAR and
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meteorological measurements (such us precipitation, cumulative precipitation, temperature
and cumulative temperature) and soil characteristics for the prediction of NDVI values after
14 days or any other preferable time window, e.g.: use the SAR and meteorological
measurements for the 30/06/2017 and predict NDVI value for 14/07/2017. This prediction
model as main aspect has to identify which are the dominant parameters that affect the
growing of the parcels for each crop type. For the prediction and feature importance we use
random forests. The higher the value of the importance for a feature the stronger the
correlation with the NDVI value.
Note that for each case the parameters importance values sum at 1.
Figure 120: NVDI per cluster
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Figure 121: Parameter importance
NDVI trends of potatoes and relation with temperature
Type of potato MAE (mean absolute error
between true NDVI values
and estimated NDVI)
Consumption 0.14
NAK 0.11
Desinfestation 0.14
TBM 0.09
Starch 0.13
An analysis of the behaviour of different types of potatoes has been performed.
Unfortunately, few observations are fully reliable due to the massive cloud coverage that
affected the Netherlands during 2017, nevertheless, different trends have been identified
(see Figure 122).
We decided also to investigate the response to high temperatures of each variety.
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Consumption potatoes
We classified consumption potatoes based on the cumulative temperature in the period 90-
200 Day of Year (from April to the middle of July) getting five groups, see Figure 123. Figure
124 shows the average NDVI profile of parcels belonging to the above mentioned 5 different
groups and it is quite clear that high temperature affects (reduces) NDVI maximum. Figure
125 shows the plot related to the average temperature for the group characterized by higher
temperature and lower maximum NDVI and for the one with lower temperature and higher
maximum NDVI.
Figure 122: NDVI profiles of different types of potato (year of reference 2017)
Figure 123: Five groups of consumption parcels based on cumulative temperature between 90 and 200 Day of Year
D1.3 – Agriculture Pilot Final Report H2020 Contract No. 732064 Final – v1.2, 21/1/2020
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Figure 124: NDVI profiles of consumption parcels according the five groups identified by the temperature analysis
Figure 125: Average temperature trends of parcels in areas characterized by higher temperatures (blue) and lower temperatures (purple)
TBM potatoes
The same approach has been followed for TBM potatoes and we got four groups based on
the cumulative temperature from 90 to 200 Day of Year, see Figure 126.
Figure 127 shows the average NDVI profile of parcels belonging to the above-mentioned four
different groups.
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Figure 128 shows the plot related to the average temperature for the group characterized by
higher temperature and lower maximum NDVI and for the one with lower temperature and
higher maximum NDVI.
Figure 126: Four groups of TBM parcels based on cumulative temperature between 90 and 200 Day of Year
Figure 127: NDVI profiles of TBM parcels according the four groups identified by the temperature analysis
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Figure 128: Average temperature trends of parcels in areas characterized by higher temperatures (blue) and lower temperatures (red)
Starch potatoes
This variety of potato seems not to be affected by high temperatures thanks to its spatial
distribution (Figure 129).
Figure 130 plots the average NDVI profile of parcels belonging to the three different groups
defined according to previous analysis.
Figure 129: Three groups of Starch parcels based on cumulative temperature between 90 and 200 Day of Year
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Figure 130: NDVI profiles of Starch parcels according to the three groups identified by the temperature analysis
NAK potatoes
The same approach has been followed for NAK potatoes and we got four groups based on the
cumulative temperature from 90 to 200 Day of Year, see Figure 131.
Figure 132 shows the average NDVI profile of parcels belonging to the four different groups.
Figure 133 shows plot related to the average temperature for the group characterized by
higher temperature and lower maximum NDVI and for the one with lower temperature and
higher maximum NDVI.
Figure 131: Four groups of NAK parcels based on cumulative temperature between 90 and 200 Day of Year
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Figure 132: NDVI profiles of NAK parcels according the four groups identified by the temperature analysis
Figure 133: Average temperature trends of parcels in areas characterized by higher temperatures (blue) and lower temperatures (red)
Intra-field analysis
The scope of the service is to analyse single parcel situation to detect the growth homogeneity
and evidencing irregular areas in the parcel, providing an indicator of field anomalies. In order
to resume the approach, a brief description of the intra-field analysis follows:
• Creation of an inner buffer within the parcel polygon in order to avoid border effects.
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• Extraction of the parcel temporal profile by calculating the mean value for each
observation.
• Identification of the observation that corresponds to the maximum growth stage of
the crop. Some filters are applied in order to exclude parcels that are not cultivated or
areas with no available images in the period of interest due to cloud cover.
• Calculation of mean value and classification of pixels within the parcel based on
thresholds.
As anticipated in D1.2, the analysis has been performed over the Netherlands considering
2017 as the year of reference. Unfortunately, the available dataset provided by the insurance
companies involved was not sufficient to study the correlation between extreme weather
events and losses, nevertheless this service is extremely useful to detect areas where
vegetation grows irregularly due to soil characteristics such as texture and depth.
Figure 134: Intra-field analysis based on NDVI spectral index with S2A and S2B data (year 2017)
Figure 135: Areas of anomalous growth
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Trial 2 results
Trial 2 results for each activity, actually reported in the previous section, are summarized here
after:
• The weather risk map service has produced good results in terms of identification over
time of areas repeatedly affected by heavy rain according the thresholds provided by
insurance companies. This approach can be also applied to further meteo-climate
variables and can help to identify and monitor high-risk areas.
• The clustering-based service has proved to be a very useful technique to identify
parcels with anomalous behaviour and to consider in a single analysis all the variables
that can affect the growth and the yield of a crop. Unfortunately, it was not possible
to validate the results due to lack of data from insurances, but the approach seems to
be very promising.
• The performed activity reveals that temperature is a factor with high impact on NDVI
of potatoes.
• Intra-field service is extremely effective in detecting soil anomalies that do not allow
crops to grow homogeneously within parcels. This service provides an indicator of soil
goodness: texture and depth, for instance, have consequences on water consumption
and on regular growth.
Components, datasets and pipelines
DataBio component deployment status
Component code
and name
Purpose for pilot Deployment status Component
location
C08.02 (Proba-V
MEP)
EO data for
historical risk
mapping
Used only in Trial 1 Proba-V MEP
at VITO
C34.01 Feature importance
applying Machine
Learning
techniques for
weather insurance
based on satellite
and meteorological
data
The component is
operational and it has
been used in Trial 2
EXUS internal
server
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C41.01 (MEA
WCS)
Extraction of
meteo data for
weather-based
risk map
(precipitation
values)
The component is fully
operational and it has
been already used in Trial
1 and Trial 2
MEEO server
C41.02 (MEA GUI) Extraction of
meteo data for
weather-based
risk map
(precipitation
values)
The component is fully
operational and it has
been already used in Trial
1 and Trial 2
MEEO server
C28.01 DataCube
Management and
preprocessing of
input EO data for
their operational
usage
The component is
operational and it is
already used in the Trial 1
and Trial 2
e-GEOS
Server
EO processing
Processing chain
for multitemporal
indices
computation from
EO data
The component is
operational and it is
already used in the Trial 1
and Trial 2
Intra-field analysis The component is
operational and it is
already used in the Trial 1
and Trial 2
Zonal statistics
tool
The component is
operational and it is
already used in the Trial 2
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Data Assets
Data Type Dataset Dataset original
source
Dataset
location
Volum
e (GB)
Velocity
(GB/year
)
NDVI data in
raster format
and
metadata
Remote
sensing data
from
Sentinel-2
optical
satellite for
2017 on 10
tiles
ESA (Copernicus
Data)
Copernicus
Scihub
https://scihu
b.copernicus
.eu/
350
SAR data in
raster format
(Amplitude
and
Coherence in
VV and VH
polarization)
Remote
sensing data
from
Sentinel-1
radar
satellite for
2017 on 3
relative
orbits
ESA (Copernicus
Data)
Copernicus
Scihub
https://scihu
b.copernicus
.eu/
1380
Vector data Netherlands
field
declarations
(2017)
Netherlands
Paying Agency
WFS Service
https://geod
ata.nationaa
lgeoregister.
nl/brpgewas
percelen/wf
s?&request=
GetCapabiliti
es&service=
WFS
1
Vector data Netherlands
Soil type
Netherlands https://ww
w.wur.nl/upl
oad_mm/1/
7/6/61a0f2a
a-4cd1-
4b5e-90db-
9498465d3b
1
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6a_BOFEK20
12_bestand
enVersie2_1
.zip
LST raster
dataset
Land
Surface
Temperature
with 0.031
degrees of
resolution
EUMETSAT
Precipitation
raster
dataset
24-hour
precipitation
accumulatio
ns from
radar and
rain gauges
KNMI https://data.
knmi.nl/data
sets/radar_c
orr_accum_
24h/1.0
0,2
Evapotranspi
ration
dataset
Daily MSG
Evapotranspi
ration
EUMETSAT
Soil moisture
raster
dataset
Sentinel1-
Soil Moisture
Copernicus
Global Land
Service
https://land.
copernicus.e
u/global/pro
ducts/ssm
Exploitation and Evaluation of pilot results
Pilot exploitation based on results
The objective for the pilot was to find useful services for the insurance to gain more insight
about the risk and the impact of heavy rain events for crops in the Netherlands. Potato-crops
are very sensitive to heavy rain, which may cause flooding of the field (due to lack of runoff)
and saturation of the soil. This may cause the loss of the potato yield in just a few days. Areas
of greater risk can be charged with higher costs for the farmer. Instead of just raising the
premium, the intention of the pilot was to be able to create awareness and incentives for
farmers to prevent losses. Therefore, the services we created served multiple purposes.
Weather is an important factor in crop insurance, because it represents a critical aspect
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influencing yield. The analysis of the long-term precipitation, categorized in threshold values,
for intense rain events, gave insight in the areas with higher risk. During climate change these
numbers may change. So, a service about the changing patterns is an interesting service.
In the pilot we looked for the relation between one single event and the potential yield loss.
For this we needed an annotated set of data, where actual losses were determined. Because
of the privacy issues related to sharing the damage data, the location of damages fields could
not be pinpointed precisely enough for correlation to the EO data. Without the details about
historical events this relationship could not be determined. Based on the information we had
though, we were able to determine the events and give information about damage risk for
other areas. A service, based on the alert that a heavy rain event took place, would be useful
for gaining insight about the impact on other locations. In order to find the most limiting
aspect in the crop development we created a dataset based on the Sentinel-2 raster size to
combine NDVI with SAR, precipitation (cumulative), temperature and soil type. The potato
type proved to be the predominant factor to predict the NDVI. Splitting up the dataset in
subsets per potato type the precipitation was the most determining factor. Unfortunately, we
couldn’t find the connection with the heavy rain, because the training set was not sufficient
for that analysis. The dataset, however, is valuable for further analysis, not limited to
insurance topics.
The consortium had a very good cooperation in a good spirit. It would be possible to continue
the cooperation in future projects based on the results of this pilot. The results are not market
ready yet, therefore there are no specific plans for joined exploitation at this moment.
KPIs
The initial set of services and activities have been reviewed and reconfigured after the analysis
of available datasets and also after the Trial 1. Some of the initially planned services (in
particular the correlation among weather historical data and critical events) were based on
the assumption to have historical dataset of losses occurred long enough to set the threshold
values and train, when necessary, the machine learning tools. The available datasets provided
by the Insurance Company involved were not sufficient to implement these approaches.
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As result, KPIs have been modified to face the situation considering the available datasets.
KPI
short
nam
e
KPI
descripti
on
Goal
description
Base
value
Target
value
Measure
d value
Unit
of
value
Comment
C1.2_
1
Spatial
Scalabilit
y
Parcel
analysis at
country level
N/A 500 1624 Km^2
C1.2_
2
Temporal
Scalabilit
y
Parcel
analysis
over time
N/A 365 365 days
C1.2_
3
Multi
source
analysis
Capabilit
y
Analysis of
multisource
data (both
EO and non
EO data)
2 5 6 Numb
er of
datas
ets
used
combi
ned
for
the
analys
is
Further
included
dataset:
SAR
C1.2_
4
Identifica
tion of
key
paramet
ers for
crop
yield
Capabilit
y
Study and
identificatio
n of
parameters
affecting
crop growth
and yield
N/A 1 2 Numb
er of
para
meter
s
identi
fied
Further
identified
paramete
rs
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14 Pilot 12 [C2.1] CAP Support Pilot overview
In the framework of EU Common Agricultural Policy (CAP), farmers can have access to
subsidies from the European Union, that are provided through Paying Agencies operating at
national or regional level. For the provision of the subsidies, Paying Agencies must operate
several controls in order to verify the compliance of the cultivation with EU regulations. At
present, the majority of the compliance controls are limited to a sample of the whole amount
of farmers’ declarations due to the increased costs of acquiring high and very-high resolution
satellite imagery. Moreover, they are often focused on a specific time window, not covering
the whole lifecycle of the agriculture land plots during the year.
The free and open availability of Earth Observation data is bringing land monitoring to a
completely new level, offering a wide range of opportunities, particularly suited for
agricultural purposes, from local to regional and global scale, in order to enhance the
implementation of Common Agricultural Policy (CAP). Nowadays, satellite image time series
are increasingly used to characterize the status and dynamics of crops cultivated in different
agricultural regions across the globe.
Pilot C2.1 CAP Support provides products and services, based on specialized highly automated
techniques for processing Big Data, in support to the CAP and relying on multi-temporal series
of free and open EO data, with focus on Copernicus Sentinel 2 data.
The main goal of the approach is to provide services in support to the National and Local
Paying Agencies and the authorized collection offices for a more accurate and complete farm
compliance evaluation - control of the farmers’ declarations related to the obligation
introduced by the current Common Agriculture Policy (CAP).
Summary of pilot before Trial 2
Trial Stage in Romania
The general methodology for Trial 1 was based on the comparison between real crop
behaviour and the expected trends for each crop typology. It involves image processing, data
mining and machine learning techniques and is based on different categories of input data:
Sentinel-2 and Landsat-8 SITS covering the time period of interest, farmers’ declarations of
intention with respect to crops types, as well as in-situ / field data.
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Figure 136: Crop families detection using Sentinel 2 temporal series
Figure 137: Pixel-based results of the analysis regarding potential incongruences with respect to farmers’ declarations stating crop types and areas covered
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Figure 138: Pilot-based results of the analysis regarding potential incongruences with respect to farmers’ declarations stating crop types and areas covered
Following the results of Trial 1, we can conclude that:
• There are well visible differences in declared crops versus crops identified through
unsupervised machine learning algorithms.
• The validation of the preliminary results against independent sources (in-situ data,
high or very high-resolution imagery) revealed promising results, with an accuracy
higher than 90% for all the selected crop families.
• There is a need for further trials, for more areas of interest, in order to compare the
results and refine parameter settings in algorithm design. Also, crop types will be used
during Trial 2 instead of crop families.
• The highly-automated proposed approach allows the performing of Big Data analytics
to various crop indicators, being reliable, cost- and time-saving and allowing a more
complete and efficient management of EU subsidies, strongly enhancing their
procedure for combating non-compliant behaviours. The developed technique is
replicable at any scale level and can be implemented for any other area of interest.
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Trial Stage in Italy
The objective of the Trial 1 has been to set up a quick methodology, based on the computation
of markers, in relation to predefined scenarios in terms of crop type and reference periods
during which agricultural practices must take place, to detect LPIS\GSAA parcel anomalies in
terms of crop type or crop family, with respect the last update (LPIS) or farmer’s declaration
(GSAA) and to re-classify the parcel itself. The methodology works at parcel level, therefore
several markers as ploughing, presence, harvesting, are computed for each parcel depending
on the specific crop type. The workflow is based on the following steps:
• Download of Sentinel-1 and Sentinel-2 satellite data from repositories. Images
collected in 2017 and 2018 have been
• Preprocessing of Sentinel-2 data in order to mask clouds and related shadows
• Generation of spectral indices from preprocessed Sentinel-2 satellite data, also by
composing data from different images, to be used for markers computation
• Intersection of Sentinel-2 spectral indices and preprocessed Sentinel-1 data with
parcels to be monitored
• Computation of markers at parcel level
Figure 139: NDVI temporal trend with identification of relevant periods
In Figure 139 has been reported for example, for a generic crop, the identification of the two
periods in which ploughing (in blue) or harvesting (in pink) event are expected for a summer
arable land crop. The results of Trial 1 highlighted that:
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• Scalability has been considered by designing a processing environment that can be
easily ported to a cloud infrastructure, therefore removing processing and storage
limitations that could be a strong barrier for enlarging at regional or national level or
more.
• Other relevant issue that has been taken into consideration is the exchange of input
and output data with the Paying Agency. This interaction requires, apart from privacy
issues, the masking of several data mainly related to property, a clear definition of
output products in terms of specifications that can properly fit the compliance
verification process. In fact, regulations for the same crop type can be different
according to the location and the adopted application schema.
• For the definition of markers, it must considered that each of them must be defined
according to the geographic location, and specific algorithm and related parameters
must be identified, therefore requiring a proper tuning by leveraging on time series
analysis. This operation is supported by the analysis, for each crop, of its spectral
behaviour along time, in order to identify from a mathematical point of view, markers
related to specific activities.
The methodology has been applied on the AOI of the project in Veneto (Varese Province)
where the LPIS 2016 was available.
Preparation and execution of Trial 2
Trial Stage in Romania
Under the framework of the DataBio project, Terrasigna ran CAP support monitoring service
trials during 2017, 2018 & 2019 for 10 000 sqkm AOI in Southeastern Romania.
The main goal of the CAP Support Monitoring pilot trials was to provide crop type maps for a
large area, characterized by geographical variability, for a broad variety of crops, distributed
over diverse location and including small and narrow plots, making use of the Copernicus
Sentinel-2 spatial and temporal resolution.
During Trial 1, developed in 2018, important sets of results have been provided, consisting of
crop families’ maps and crop inadvertencies maps. The results were based on farmers’
declarations regarding crop types and areas covered for 2017 and 2018 agricultural seasons
and involved five crop families: wheatlike cereals, maize-like cereals, sunflower and related
crops, rapeseed and related crops, grassland, pastures and meadows. The results delivered
for the 2018 agricultural season have been validated through a series of in-situ data and
Sentinel-2 backgrounds for the test-area, resulting in a qualitative assessment, used in order
to define Trial 2 actions and expected results.
The main goal of Trial 2 was to overview the key results for Trial 1, identify the emerging needs
of the components that were involved in Trial 1 and provide a further development of the
Crop Monitoring Service, with products tuned in order to fulfil the requirements of the 2015-
20 EU Common Agricultural Policy.
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Therefore, Trial 2 was based on an adjusted version of the crop detection algorithms, updated
according to the previous validation work, aiming to increase accuracy and success rate.
Terrasigna's proposed methodology has undergone continuous development and
improvements over the last 4 years, now reaching version v.05 of the algorithms.
Apart from the optimised version of the algorithms used, Trial 2 objectives included:
• New measurements carried out for the analysed area, according to a field tracking
plan;
• The delivery of new results for the same area of interest, but based on a higher
number of target crops and using crop types instead of crop families;
• Further testing of the algorithms developed and extension of the service at national-
scale level.
Trial Stage in Italy
Starting from the preliminary results of Trial 1, the main objective of Trial 2 was to refine and
validate the approach implemented. For these purposes the Trial 2 activities included:
• Refinement of the criteria adopted to aggregate the crops in crop families;
• Refinement of the rules adopted for the marker computation;
• Collection of validation data for accuracy assessment
• Validation of results
Trial 2 timeline
Trial Stage in Romania
Trial 2 activities have been mainly divided into 3 steps:
Step 1: Trial 2 Start (M26)
Trial 2 started with the DataBio components ready for pilot trial implementation, as well as
platform services ready for use in the final pilot iterations. All the specifications for Agriculture
Pilots Trial 2 have been defined within internal deliverable D1.i2 – `Agriculture Pilots Trial 2
Specifications`.
Step 2: All pilot services have been developed and ran between M26 and M34. On average,
the frost-free growing season in Romania starts at the beginning of April and ends by the
beginning of November. However, most of the target crops used in the analysis performed
within the pilot are harvested by the end of September. Therefore, final results were available
by mid-October (M34). Pilot services included:
• Further adjustments of the crop detection algorithm, based on Trial 1 results, fine
tuning and comparisons based on the results obtained in Trial 1;
• Dialog with users / beneficiaries / stakeholders (APIA - the Romanian National Paying
Agency);
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• Ingestion of new farm profile data (farmers declarations for 2019) and related Earth
Observation data (Sentinel-2, Landsat-8) acquired for the 2019 growing season;
• Further development of the Crop Monitoring Service, using the 2018 and 2019
farmers’ CAP declarations regarding crop types and areas covered;
• Accuracy level computation;
• Comparisons with Trial-1 results;
• Development and extension of the service at national-scale level, for the whole
territory of Romania, for both 2018 and 2019 agricultural seasons.
Step 3: Trial 2 End (M34)
The goals obtained have been Final implementation of pilot activities (including results’
delivery), final analytics, final pilot KPI measurements for Trial 2 and collection of feedback
from pilot stakeholders.
Figure 140: Trial 2 timeline of Romanian AOI in pilot C2.1
Trial Stage in Italy
M24-M30: Collecting historical S2 data (2017-2018) on the AOI (Verona Province, Italy) and
refinement of methodology (marker’s rules and crop type aggregation).
M31-M36: Running the prototype, analysis and validation of results.
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Figure 141: Trial 2 timeline of Italian AOI in C2.1
Preparation for Trial 2
Preparation of Trial Stage in Romania
Trial 2 started with fine tuning of algorithms. Various parameters have been tested in order
to implement a final version of the crop-type detection algorithm. The algorithms have been
modified in order to increase the number of analysed crop types and the overall accuracy of
the results.
The preparatory work for Trial 2 has focused on a preliminary collection and analysis of the
data needed for running the Romanian pilot.
Data collection activities included three main categories:
1. External data – farmers’ declarations: Pilot C2.1 CAP Support uses farmers’
declaration regarding crop types and areas covered as input data. These data are
provided by the Romanian National Paying Agency, as well as its regional offices. In
2019, the deadline for collecting the declarations was May 15th. After this deadline,
an approximately 2-weeks interval was needed in order to process the data before
delivering it to TERRASIGNA. Therefore, the proper sample processing and ingestion
of farmers’ declarations for 2019 started in June and the processing and analysis stage
ran over the next four months, from June until the beginning of October. For the
10,000 sqkm area of interest, more than 150,000 plots of different sizes have been
analyzed during the 2019 agricultural season. The analysis performed included parcels
of over 0.3 ha, regardless of shape. Of course, the 10-meters spatial resolution made
the narrower parcels difficult to properly label. Very related groups of cultures, which
have synchronous phenological evolutions and similar aspect have been grouped into
crop classes.
What is more, for the 2018 and 2019 agricultural years, Terrasigna extended its CAP
monitoring service and monitored the declarations for the entire agricultural area of
Romania. The total surveyed area exceeded 9 million ha, corresponding to more than
6 million plots of various sizes and shapes. The necessary Earth Observation (EO) data
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required multiple Sentinel-2 scenes projected in 2 UTM zones. 21% of the total
number of plots within the test areas have surfaces below 1 ha.
The observed crop types maps included 32 crops, summing more than 98% of the total
declared area.
Figure 142: Structure of the data for the 10,000 sqkm area of interest
Figure 143: Agricultural land plots for the 10,000 sqkm area of interest. Data Source: Agency for Payments and Intervention in Agriculture (APIA), Romania
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Figure 144: Romania - total declared area and number of plots registered for CAP support (2019). Data Source: Agency for Payments and Intervention in Agriculture (APIA), Romania
2. Optical Earth Observation (EO) data: Landsat-8 OLI and Sentinel-2 MSI - both Sentinel-
2A and Sentinel-2B have been downloaded for the area of interest, for a time interval
between March and September 2019. The 10-meter spatial resolution of the Sentinel-
2 data enables the survey of the smaller plots that in Romania represent a significant
number of CAP applications. The spectral resolution provides all the necessary
information (visible, NIR, SWIR) for observing the crop phenology. On a more general
note, TERRASIGNA’s technology uses both Copernicus Sentinel-2 and Landsat 8
imagery for a maximum of information availability and time series density compared
to using only Landsat 8 or Sentinel 2 images separately.
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3. Field data: Field data have been collected according to a field tracking plan. Also, this
category includes different datasets provided by the Agency for Payments and
Intervention in Agriculture (APIA), based on the annual on-site compliance
verifications of the farmers that applied for subsidies. All the field data have been used
as independent validation data.
Preparation of Trial Stage in Italy
As prosecution of Trial 1, the first part of preparatory work of Trial 2 has been focused on four
main activities aimed to finalize the preparation of the trial input data:
Satellite data collection:
• Sentinel-2 time series over the AOI: collection, cloud, snow and shadow masking and
vegetation index extraction (NDVI) of Sentinel-2 data acquired from May 2017 to
December 2018, related to the granule T32TPR. Temporal aggregation of NDVI data
over an interval of 20 days.
LPIS 2016 data analysis and crop type aggregation:
• Analysis of crop types of the AOI and refinement of the LPIS macro classification:
aggregation in macro classes (23 families) and analysis of classes distribution.
• Selection of crop classes suitable for the automatic detection of anomalies and re-
classification, based on the Sentinel-2 time series. Largest part (about 67%) of AOI
agricultural crop families belong to 2 main groups: permanent grassland and arable
land. The crop families of these 2 groups have been considered to test the algorithm
of anomalies detection and re-classification at macro-class level. The anomalies
analysis on the group of permanent crops have been focused on the detection of
explant cases.
Figure 145: LPIS crop families distribution
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Figure 146: LPIS legend with crop type aggregation in macro classes
Validation data collection:
• Collection of a validation dataset, representative of the crop families distribution
(mainly permanent grassland, winter and summer arable land, temporary grassland),
from very high resolution imagery
Marker rules refinement:
• Refinement of markers rules and their computation: markers have been defined and
computed in relation to predefined scenarios in terms of selected macro crop type
reference periods and related thresholds during which agricultural practices must take
place (e.g. ploughing, presence\growth and harvesting)
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Figure 147: Summary of markers periods for each macro class of crop type
All the listed markers are computed for each macro class. This is necessary in order to allow,
as much as possible, the assignment of a new macro class (re-classification) in case of anomaly
detection.
Trial 2 execution
Execution of Trial Stage in Romania
Trial 2 execution for the Romanian area of interest was entirely based on Terrasigna's toolbox
for crop determination, consisting of a set of in-house developed algorithms for calculating
CAP support-related products. Following an automatic learning process, the system becomes
capable of recognizing several types of cultures, of the order of several tens. The processing
chain used during Trial 2 included the following activities:
A) Data Ingestion
Earth Observation data used within the framework of the CAP Support Pilot is derived from
two different sensors, which requires an effort to harmonize the spatial resolution and the
footprint of the native pixel grids. The ingestion process involves the following important
steps:
• Unzipping raw data (Sentinel2 and Landsat 8 data, not atmospherically corrected);
• Harmonizing data covering the area of interest by using a common numeric format
and a tiles system;
• Automatic co-registration / georeferencing corrections;
• Cloud and shadow masking and extraction of masks of areas of interest.
B) Scene classification
• Use of statistical parameters for the crop classification (obtaining the native structure
of semantic clusters and applying them at tile level);
• Granting of semantic profile for the individual classified scenes (the pixels get the fuzzy
labels belonging to the crop class).
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C) Time series analysis
• Building the time series of semantic profiles, at tile level;
• Defuzzification` and application of a filter to reduce confusion between crop classes
D) Construction of graphical products and analytical data
• Concatenation of tile-level results;
• Delivery of single channel or RGB maps illustrating crop types, crop compliance,
classification confidence etc.;
• Extraction of numerical, quantitative syntheses based on the delivered products.
Execution of Trial Stage in Italy
The activities and the services that have been set up in the Trial 2 are briefly described here
after:
Anomalies detection
The markers computed in relation to predefined scenarios in terms of crop type, reference
periods and specific thresholds, during which agricultural practices must take place, have
been implemented in a decision model to verify parcel’s correct classification. The model has
been run for each parcel of the macro-classes considered as suitable for the automatic
detection of anomalies.
Here below some examples of parcels for which the original macro class has been confirmed
through the automatic analysis based on the related markers or that have been detected as
anomalous.
Figure 148: Examples of verified (left) and not verified (right) autumn-winter arable land parcel
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Figure 149: Examples of verified (left) and not verified (right) summer arable land parcel
Figure 150: Examples of verified (left) and not verified (right) Temporary Grassland parcel
Re-classification of LPIS anomalous parcels
Parcels detected as anomalous have been automatically re-classified testing the validity of
the markers of the other macro classes. Here below some examples.
Figure 151: Examples of not verified (left) Autumn-Winter arable land re-classified as Summer arable Land (right)
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Figure 152: Examples of not verified (left) Summer arable land re-classified as Artefact (right) due to the presence of a new building
Trial 2 results
Trial 2 Results for the Romanian AOI
During Trial 2, Terrasigna's toolbox for crop determination and monitoring involved automatic
procedures for calculating the following products:
• Maps of the main types of crops, for an annual agricultural cycle completed;
• Intermediate maps with the main types of crops, during an annual agricultural cycle
(they may serve as early alarms for non-observance of the declared crop type);
• Early discrimination maps between winter and summer crops;
• Layers of additional information, with the degree of confidence for the crop type maps
delivered;
• Maps of the mismatches between the crop type declared by the farmer and the one
observed by the application;
• NDVI maps nationwide for a period of time, uncontaminated by clouds and cloud
shadows;
• Lists of parcels with problems, in order of the surfaces affected by inconsistencies;
• National maps with RGB aspect mediated for a period of time, uncontaminated by
clouds and shadows, obtained through the use of components C39.01 - Mosaic Cloud
Free Background Service and C39.03 - S2 Clouds, Shadows and Snow Mask Tool.
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Figure 153: Example of CAP Support Analysis - Trial 2 results
Figure 154: Trial 2 results. Observed crop type map (2019) for the area of interest in Southeastern Romania
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Figure 155: Trial 2 results. Observed crop type map (2019) for the entire territory of Romania
VALIDATION
The validation stage consisted in two different types of activities:
• Independent validation activities, performed against very-high resolution imagery and
other data sources, mainly field-collected data;
• Validation using reference data provided by APIA - the Romanian National Paying
Agency.
Independent validation activities, performed against very-high resolution imagery and other
data sources, took into account more than 5,800 plots, with a total surface of more than
77,000 ha. The validation work has shown 98.3% correct estimations for 8 crop categories:
winter wheat, maize, sunflower, soybean, rapeseed, hayfields, peas and winter barley. There
can be noticed an increased performance for larger plots (more than 99% for all 8 crop
categories for plots larger than 20 ha).
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Figure 156: Results of the validation based on independent data consisting of very-high resolution imagery and field-collected data
Validation using reference data provided by APIA - the Romanian National Paying Agency
The Agency for Payments and Intervention in Agriculture (APIA) performs annual verifications
of the farmers that applied for subsidies using “classical” on-site compliance verifications, as
well as remote sensing-based checks. The reference plots used for the validation activities
cover the entire area of Romania eligible for CAP support and vary in terms of declared area.
For each plot, a dominant crop code (corresponding to Terrasigna’s crop codes system) was
assigned, provided it covers more than 40% of the plot’s area. Therefore, the validation
focused on the 32 predominant crops. Data have been then intersected with Terrasigna’s
observed crop type maps and finally joined with the initial set of declarations. This part of the
validation activities took into account more than 16,000 plots, with a total surface of more
than 60,000 ha and the results have been broken down for 7 plot classes:
• Very small plots: <0.5 ha, 0.5-1 ha;
• Small plots: 1-2 ha, 2-5 ha;
• Medium plots: 5-10 ha, 10-20 ha;
• Large plots: >20 ha.
Validation using reference data provided by APIA showed a 97.28% accuracy percent for the
32 crops assessed, also noticing an increased performance for larger plots (more than 99%
for plots larger than 20 ha).
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Figure 157: Results of the validation based on reference data provided by APIA - the Romanian National Paying Agency
Results of Trial Stage for the Italian AOI
During Trial 2, e-GEOS generated automatically the following products based on automatic
procedures:
• Maps of the anomalies between the crop type declared by the farmer and the one
observed by the application;
• Updated LPIS after the re-classification of the anomalies, for the macro crop classes
considered
Here below the product’s examples on 2 areas of interest characterized by a different
agricultural prevalent use: arable land and permanent grassland.
As expected in the arable land area, due to the usual crop rotation practice, the largest part
of parcels changed their agricultural use between 2016 and 2018 (Figure 159). In most cases
it is simply a change from winter-autumn to summer or temporary grassland and vice versa
(Figure 159).
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Figure 158: LPIS parcel classified according to verified parcels (in green), anomalous parcels (in red) and not analyzed parcels (in grey) - Arable land area
Figure 159: LPIS parcels type 2016 (left) and 2018 (right) after re-classification of anomalous parcels - Arable land area
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This is confirmed also by the following pie charts that describe, for different crop families
(autumn-winter arable land, summer arable land and irrigated summer arable land), the
percentage of parcels having the crop family confirmed (percentage number in green) and
the percentages of parcels not confirmed, re-classified as other crop families.
Figure 160: 2016 LPIS Summer arable land parcels update to 2018
Figure 161: 2016 LPIS Winter-Autumn arable land parcels update to 2018
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Irrigated summer arable land parcels (e.g. rice paddies) are mostly confirmed (few anomalies)
probably because these types of crop field, supported by irrigation systems, are not subject
to crop rotations.
Figure 162: 2016 LPIS Irrigated summer arable land parcels update to 2018
For what concerns the permanent grassland area, as expected, the percentage of anomalies
is meaningful lower because usually the agricultural use of these parcels is stable for several
years (a grassland field, according to common regulations, is defined as permanent if it is not
ploughed for 5 years, at least).
Figure 163: LPIS parcel classified according to verified parcels (in green), anomalous parcels (in red) and not analyzed parcels (in grey) - Permanent grassland area
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Figure 164: 2016 LPIS Permanent grassland parcels update to 2018
Permanent crops have been analysed using markers finalized to detect explant events. The
percentage of explants is low (<1%). Here below an example of a vineyard parcel, present
since 2012, explanted on March 2018.
*Google Earth
Figure 165: Example of NDVI temporal trends (2017-2018) of a vineyard parcel explanted on March 2018.
The accuracy of the methodology proposed for the LPIS anomalies detection and re-
classification has been assessed through a validation activity based on reference data
extracted from very high-resolution imagery.
About 1000 parcels, on a total amount of 18.283, corresponding to 7.5% of total hectares,
have been considered for the accuracy assessment. The resulting validation dataset was
composed by 4 main crop families (Autumn winter arable land, Summer arable land,
Permanent grassland and Temporary grassland), reflecting the crop families distribution of
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the entire area. The other crop families were not represented by a number of parcels
meaningful from a statistical point of view, therefore they have not been considered in the
accuracy assessment.
Crop family Parcel number Accuracy (%)
Autumn winter arable land 26 84.6%
Summer arable land 55 96.4%
Permanent grassland 973 96.5
Temporary grassland 73 38.2%
Figure 166: Results of the validation based on reference data extracted from very high-resolution imagery
The results show that the accuracy is quite high for permanent grassland and summer arable
land (more than 95%), high for winter arable land (85%), but for what concerns temporary
grassland crop family, with respect the farmers’ declarations, just about 40% are confirmed.
The remaining 60% mis-classified are distributed, according to farmers’ declarations, mainly
as permanent grassland (33%) and they require an additional refinement of marker rules to
improve the accuracy.
Components, datasets and pipelines
DataBio component deployment status
Component code
and name
Purpose for pilot Deployment
status
Component location
C07.01 - FedEO
Gateway
Data Management
(Collection, Curation,
Access) – EO
Collection Discovery,
EO Product Discovery,
Catalog, Metadata
Operational
component, used
in both Trial 1 and
Trial 2 (for the
Romanian AOI), in
combination with
FeoEO Catalog
and Data
Manager.
Owner: Spacebel
Visibility: visible to
project
The component is a
Java application that
can be made available
as software or can be
provided as a service
hosted by Spacebel.
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C07.03 - FedEO
Catalog
The component was
used in combination
with the FedEO
Gateway and Data
Manager to setup a
complete chain to
retrieve and index
Sentinel-1, Sentinel-2
or Landsat data and
other data available
through FedEO on a
local processing
platform.
Operational
component,
deployed on an
application
server, used in
both Trial 1 and
Trial 2 (for the
Romanian AOI), in
combination with
FeoEO Gateway
and Data
Manager.
Owner: Spacebel
Visibility: visible to
project
This component is
deployed on an
application server
(Tomcat) and can be
accessed by any client
application
implementing the
API.
C07.04 - Data
Manager
The component will
be used in
combination with the
FedEO Gateway and
FedEO Catalog to
setup a complete
chain to retrieve and
catalog Sentinel-1,
Sentinel-2 or Landsat
data (SciHub and
CMR/USGS) and other
data available
through FedEO on a
local processing
platform.
Operational
component,
deployed on an
application
server, used in
both Trial 1 and
Trial 2 (for the
Romanian AOI), in
combination with
FeoEO Gateway
and FedEO
Catalog.
Owner: Spacebel
Visibility: visible to
project
This component is a
Java application
(.war) deployed on an
application server
(GlashFish).
Can be made
available as software
to be deployed in
combination with
FedEO Gateway
component (to access
remote catalogs) and
FedEO Catalog (to
store metadata).
C39.01 - Mosaic
Cloud Free
Background
Service
Data management
and Data curation -
keeping an up to date
collage (mosaic) of
Sentinel-2 and
Landsat-8 images,
covering the area of
interest (AOI) with the
latest, cloud free
Operational
component.
Adjusted
according to the
needs of pilot
C2.1 CAP Support
(trials for the
Romanian AOI).
The component is
deployed on an
application server and
provides a remote
sensing monitoring
service developed in-
house by Terrasigna.
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satellite scenes; the
fusion and
harmonization
between images are
made only at RGB
level, mainly for eye
inspection, but also
for other possible
advanced processing;
the whole process
chain is independent
and self-content,
based on cloud and
shadows mask
extraction, histogram
matching procedures
and, finally, a pixel
based analysis.
Backgrounds will be
updated
automatically, soon
after a new raw scene
is available during the
whole Trial 2 period.
The service can run on
Linux server,
delivering results via
WMTS.
C39.02 - EO Crop
Monitoring Service
Descriptive analytics –
EO data processing.
The component is
able to assess the
agriculture parcels
from satellite data
and farmers’
declarations in order
to create a series of
products like, Crop
masks, Parcels used
maps and Crop
inadvertencies maps,
based on SITS -
Satellite Image Time
Series.
Operational
component.
Adjusted
according to the
needs of pilot
C2.1 CAP Support
(trials for the
Romanian AOI).
Service hosted by
Terrasigna. The
component is running
on a Linux server.
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C39.03 - S2 Clouds,
Shadows and
Snow Mask Tool
Data curation - EO
data preprocessing.
The tool produces
Sentinel-2 Clouds,
Shadows and Snow
Masks, based only on
raw data, improving
the results of the
genuine quality
assessment band. The
results are raster
maps (GeoTiff) with 4
label codes: 0 – for no
data, 1 – for
uncontaminated/ free
pixels, 2 – for snow, 3
– for shadows and 4 –
for clouds.
Operational
component.
Adjusted
according to the
needs of pilot
C2.1 CAP Support
(trials for the
Romanian AOI).
A stand-alone
executable file was
prepared for Linux
environment and is
deployed on
Terrasigna’s servers.
C28.01 DataCube
Management and
preprocessing of
input EO data for their
operational usage
The component is
operational and it
is already used in
the Trial 1
e-GEOS Server
EO processing
Processing chain for
multitemporal indices
computation from EO
data
Markers engine
Computation of
markers at
agricultural parcel
level
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Data Assets
Data Type Dataset Dataset
original
source
Dataset
location
Volume
(GB)
Velocity
(GB/year)
Optical
satellite
imagery
Landsat-8
OLI
NASA - USGS
(U.S.
Geological
Survey) –
accessed via
USGS Earth
Explorer
Terrasigna’
s servers
(local
storage)
Trial 2
(2019):
approximat
ely 35 – 40
GB
Trial 1
(2017+2018
):
approximat
ely 60 GB
2017 - 2019
(Trial 1 +
Trial 2):
approximat
ely 100 GB
approximately
35 GB/year
(the pilot area
is covered by 3
Landsat-8 tiles
- 181/29,
182/29, 183-
29, with a 16-
days revisit
time;
approximately
40 Landsat-8
scenes used for
each
agricultural
season; each
archive
containing 185
km X 170 km
tiles is about
900 MB)
Optical
satellite
imagery /
Copernicus -
Sentinel
Sentinel-
2 MSI -
both
Sentinel-
2A and
Sentinel-
2B
ESA
(Copernicus
Data), via
Copernicus
Open Access
Hub
Terrasigna’
s servers
(local
storage)
Trial 2
(2019):
approximat
ely 90 GB
Trial 1
(2017+2018
):
approximat
ely 140 GB
2017 - 2019
(Trial 1 +
Trial 2):
approximately
85 GB/year
considering
the full
constellation
(Sentinel-2A +
Sentinel-2B)
(the pilot area
is covered by 2
Sentinel-2 tiles
- 35TMK and
35TNK, with a
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approximat
ely 230 GB
5-days revisit
time;
more than 120
Sentinel-2
scenes used for
each
agricultural
season; each
archive
containing 100
km X 100 km
tiles is about
700 MB)
In-situ data In-situ
data
Field data Terrasigna’
s servers
(local
storage)
Trial 2:
approximat
ely 100 MB
Trial 1
(2017+2018
):
approximat
ely 100 MB
2017 - 2019
(Trial 1 +
Trial 2):
approximat
ely 200 MB
approximately
100 MB/year
Farm profile
data
Farm
profile
data -
farmers'
declarati
ons
regarding
crop
types and
area
covered,
for each
APIA (Agency
for Payments
and
Intervention
in Agriculture)
- Romanian
National
Paying Agency
Terrasigna’
s servers
(local
storage)
Trial 2:
approximat
ely 150 MB
(farmers'
declarations
for 2019)
Trial 1:
approximat
ely 150 MB
(farmers'
declarations
approximately
150 MB/year
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agricultur
al season
for 2017 and
2018)
2017 - 2019
(Trial 1 +
Trial 2):
approximat
ely 300 MB
Optical
satellite
imagery /
Copernicus -
Sentinel
Sentinel-
2 MSI -
both
Sentinel-
2A and
Sentinel-
2B
ESA
(Copernicus
Data), via
Copernicus
Open Access
Hub
e-GEOS
servers
(local
storage)
2017 - 2018:
approximat
ely 290 GB
approximately
170 GB/year
considering
the full
constellation
(Sentinel-2A +
Sentinel-2B)
and the raw
data (.safe)
and NDVI
(the pilot area
is covered by 1
Sentinel-2 tiles
- 32TPR, with a
5-days revisit
time;
Vector data LPIS
Verona
Province
Italian Paying
Agency
e-GEOS
servers
(local
storage)
60 MB 60 MB
Tables Activity
markers
for
agricultur
al fields
e-GEOS e-GEOS
servers
(local
storage)
100 KB 100 KB
Exploitation and Evaluation of pilot results
Pilot exploitation based on results
The highly-automated fuzzy-based proposed approach developed by Terrasigna for the
Romanian AOI used within the C2.1 CAP Support pilot allows the performing of Big Data
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analytics to various crop indicators, being reliable, cost- and time-saving and allowing a more
complete and efficient management of EU subsidies, strongly enhancing their procedure for
combating non-compliant behaviours. Terrasigna's proposed methodology has undergone
continuous development and improvements over the last 4 years. A further development of
the Crop Monitoring Service is able to provide products tuned in order to fulfil the
requirements of the 2015-20 EU Common Agricultural Policy. The developed technique is
replicable at any scale level and can be implemented for any other area of interest.
The methodology proposed by e-GEOS is a quick approach to detect the LPIS anomaly of some
crop families mainly related to arable land (winter and summer arable land) and temporary
and permanent grassland. The performance and the usefulness of the approach marker-
based could be improved by using more refined marker’s rules in order to be able to analyse
single crop types, reducing the need to aggregate them in macro classes.
KPIs
KPI short
name
KPI
description
Goal
descriptio
n
Base
value
Target
value
Measured
value
Unit of
value
C2.1_1
(Values
measured
for the
Italian
AOI)
Percentage of
LPIS area
processed vs
global LPIS
coverage in
terms of
hectares
Agricultural
territory
coverage
N/A 50% 71% %
C2.1_2
(Values
measured
for the
Italian
AOI)
Percentage of
parcels > 0.5
hectares that
are processed
Small parcel
size
capability
N/A 80% 98% %
C2.1_3
(Values
measured
for the
Italian
AOI)
Parcel
anomalous
that are not re-
classified
Re-
classificatio
n
performanc
e
N/A 10% 2% %
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C2.1_4
(Values
measured
for the
Romanian
AOI)
Processed
surface
Agricultural
territory
coverage
N/A 10 000 Trial 1:
10 000 km2
Trial 2:
130 000 km2
(whole
country)
sqkm
C2.1_5
(Values
measured
for the
Romanian
AOI)
Number of
crop types
addressed
Diversity.
Ability to
recognize
different
crop
cultivation
patterns
NA 5 Trial 1: 5 crop
families
Trial 2: 32
crop types
crop
types
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15 Pilot 13 [C2.2] CAP Support (Greece) Pilot overview
NEUROPUBLIC and GAIA EPICHEIREIN have launched a highly ambitious pilot in Northern
Greece in an area covering 50000ha, targeting towards the evaluation of a set of EO-based
services designed appropriately to support specific needs of the CAP value chain stakeholders.
The pilot services rely on innovative tools and complementary technologies that will sustain
the interconnection with IoT infrastructures and EO platforms, the collection and ingestion of
spatiotemporal data, the multidimensional deep data exploration and modelling and the
provision of meaningful insights, thus, supporting the simplification and improving the
effectiveness of CAP. The pilot activities aim at providing EO-based products and services
designed to support key business processes including the farmer decision-making actions
during the submission of aid application and more specifically leading to an improved
“greening” compliance. The ambition of the current pilot is to deal effectively with CAP
demands for agricultural crop type identification, systematic observation, tracking and
assessment of eligibility conditions over a period of time. The pilot activities are fully aligned
with the main concepts of the new agricultural monitoring approach which will effectively
lead to fewer controls, will facilitate and expand the adoption of technology to the farmer
communities, will promote the penetration of EO deeper into the CAP line of business and
raise the awareness of the farmers, agronomists, agricultural advisors, farmer cooperatives
and organizations (e.g. groups of producers), national paying agencies (e.g. OPEKEPE) on how
new technological tools could facilitate the crop declaration process. The pilot will mainly
focus on annual crops with an important footprint in the Greek agricultural sector (rice,
wheat, cotton, maize, etc.). The main stakeholders of the pilot activities are the farmers from
the engaged agricultural cooperatives in the pilot area and GAIA EPICHEIREIN that has a
supporting role in the farmers’ declaration process. CSEM and FRAUNHOFER are also involved
in the pilot providing their long-standing expertise in the technological development
activities.
Summary of pilot before Trial 2
The pilot has completed the first round of trials during Trial 1 in the greater area of
Thessaloniki, Greece. It effectively demonstrated how Big Data enabled technologies and EO-
based services can support specific needs of the CAP value chain stakeholders and more
specifically the systematic and more automatic assessment of eligibility conditions for
“greening” aid declarations. Lessons-learnt from Trial 1 are valuable and critical for delivering
even more accurate solutions. Certain technical considerations have been reported, in
respect to the followed methodology and especially its applicability in challenging datasets
comprised of new and unseen data for the trained crop models. By following a systematic and
exhausting data screening parallel activity, it was identified that inter-year changes in crop
cultivating periods (begin, end, peak, length) should be deeply considered. These inter-year
changes are mostly deriving from climate changes, regulatory and market conditions, regional
characteristics etc. Major effort is underway by pilot partners to exploit new data, features
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and classification methodologies that take into account all the above and deliver even better
pilot results. Moreover, in Trial 2 new visualization tools will be explored that could handle
nice-to-have features such as intra-parcel crop classification results (pixel level) and validation
of the classification outcomes. To this end, FRAUNHOFER will expand its suite of provided
tools for the DataBio pilots (until now for pilots A1.1, B1.2, C1.1 of WP1) in order to cover
specific needs of C2.2 pilot.
Figure 167: Geographical distribution of the parcels that take part to the pilot C2.2 activities
Preparation and execution of Trial 2
Trial 2 timeline
The following roadmap applies for the pilot activities:
Figure 168: C2.2 pilot timeline
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Preparation for Trial 2
The following work was conducted by NP, as part of the preparatory work for Trial 2:
• As the requirements in terms of sensors deployed for in-the-field usage differ between
pilot sites, it became obvious that several adaptations were necessary in respect to
C13.03 and the way data was represented for both cloud-based storing and Gaiatron
station configuration. More specifically, all relational and EAV (Entity-Attribute-Value)
data representations were adapted to more flexible and scalable JSON format that
performs better in a dynamic IoT measuring environment. The latter is widely
acknowledged as JSON has become gradually the standard format for collecting and
storing semi-structured datasets that originate from IoT devices. The adaptation to a
JSON format for modelling IoT data streams allows the further processing, parsing,
integration and sharing of data collections in support of system interoperability
though the adaptation on well-established and favoured linked-data approaches
(JSON-LD).
• Lessons-learnt from Trial 1 led to C13.02 GAIABus DataSmart Machine Learning
Subcomponent’s advancement in two ways:
1. Methodologically, deep convolutional neural networks have been explored that
have proven to outperform classical machine learning classification methods.
Crop classification is performed into “super” classes or major crop types. The
model will predict the tested parcels crop type, giving specific probability. The
eligibility status will be visualized by the system of traffic lights at parcel level,
2. In terms of EO data, Sentinel-2 derived NDVI measurements from multiple years
(2016, 2017 and 2018) are available for the region of interest, thus, offering a
strong multi-year data record for building EO-based crop models that capture
inter-year trends and changes that hindered crop classification accuracy in Trial 1.
During the preparatory phase of Trial 2, CSEM continued on improving the accuracy of its
C31.01 Neural Network Suite for specific crop classes that can be considered a baseline for
future crop modelling activities. As a first step, a structured method of digitizing expert
knowledge in a data-driven architecture was offered. A pipeline was developed significantly
reducing the complexity of creating models by removing the need of hand-crafted filtering,
making it a cost-effective option for bringing neural network models to the market. It was
identified that is was important to verify the reliability of the data with minimum supervision
and then, use the clean data to train the network for the classification problem at hand. All
the efforts, led to an overall accuracy in terms of classification over 92% for Maize, Wheat
and Legumes. Further investigation on particular taxonomical varieties found that training a
crop model with one variety and testing with other varieties performed well, apart from the
crop type Legumes, which shows large intra-class variability. This aspect of creating a model
with only one variety has the potential to simplify the creation of models in the future. As this
methodology is pixel-based, pixel probabilities are aggregated into parcel-level binary result
that provides exact fit for the CAP Support use case. In particular, a parcel is assigned to a
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particular crop type label (classified) if the majority of the parcel pixels have a probability to
belong to the class greater than a given threshold (i.e. 0.5).
The preparatory work conducted by FRAUNHOFER for Trial 2 concentrated on the discussion
how existing analytic services could be integrated into a web-based analytic-platform with
ease. The starting point for this was provided by the solution developed during Trial 1. During
this discussion, a variety of ideas were developed how different services could be integrated
into a single platform, which is also able to cope with multiple data sources, to fulfil the needs
of specific use-cases. One of the major challenges of such efforts is to reduce the complexity
of integration. Principles of modern architecture styles such as Self-contained Systems (SCS)
(https://scs-architecture.org) or Microservices were considered to promote the separation
into independent components. Each component consists of capabilities to access data,
process or analyse it and consequently visualize the result. The integration itself must be done
at the UI-Layer. Following this approach provides more flexibility and eventually allows
thinking about a platform which enables the users to build views for custom analytic tasks
composed by a variety of components. The horizontal impact of this stage can provide
solutions for multiple scenarios spanning from Smart Farming, to CAP support and Agri-
Insurance.
The implementation of Trial 2 focuses primary on the integration of external services. In this
scenario a web-application was developed to enable professional users - to do crop type
classification on demand using latest or historic satellite images. A variety of visual analytic
tools are included to allow efficient exploration of available data. The functional capabilities
for the purpose of classification are offered by external services which in turn exploit methods
from the domain of machine learning (ML). The integration of services and data sources is
done using well-defined RESTful interfaces.
Trial 2 execution
During Trial 2 the following actions have been performed by the partners involved in the pilot
activities:
By M26, the DataBio platform v2 for the pilot is fully operational and offers a valuable error
checking tool for assessing “greening” compliance.
By M28, a preliminary architecture for FRAUNHOFER’s analytics platform has been drafted.
The platform was the main discussion topic during the M28 DataBio Thessaloniki Codecamp,
hosted in NEUROPUBLIC’s N. Greece offices with the participation of other DataBio partners
involved in the WP1 pilots led by NEUROPUBLIC. Furthermore, the generalization and simple
adaptation to other scenarios was discussed intensively.
By M32, a first instance of the aforementioned analytics platform has been finalized and
deployed. The use of ML services is available providing a proof of concept for its use in CAP
Support scenarios. FRAUNHOFER was responsible for the development of the UI, integrating
map, pixel heat maps from the different classifiers and information visualization capabilities
(Figure 169, Figure 170).A CSEM developed system for the management of Machine Learning
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models was used to facilitate the simple and retraceable management of models. RESTful
services, combined with security features in the form of JWT tokens and encryption with
HTTPS, were implemented and integrated into service. The service has also been
containerized to allow simple deployment. This service enables the communication with the
FRAUNHOFER’s component GeoRocket and UI for the on-demand classification, in both pixel
and parcel levels, of crop types.
Figure 169: FRAUNHOFER's UI screenshot colour coding different crop types
Figure 170: FRAUNHOFER's UI screenshot that integrates CSEM’s classification results into pixel heat maps
By M34, the assessment of “greening” compliance begins for the current year’s (2019) aid
applications. The crop types that have been modelled and tested by C13.02 GAIABus
DataSmart Machine Learning Subcomponent are seven (7) in total and more specifically:
cereals, cotton, maize, tobacco, rapeseed, rice and sunflower and correspond to the area of
interest (Thessaloniki, Greece region). If seen as multi-class classification problem the
performance of the trained crop models to the testing 2019 data are offered at the following
table and the confusion matrix respectively:
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Table 8: Crop classification results
PRECISION RECALL F1-measure ACCURACY
Maize 0.994 0.932 0.945 0.986
Cotton 0.990 0.954 0.961 0.982
Rapeseed 1.000 0.713 0.833 0.997
Sunflower 0.985 0.823 0.818 0.974
Tobacco 0.999 0.712 0.762 0.996
Rice 0.999 0.994 0.993 0.999
Cereals 0.952 0.967 0.958 0.959
Figure 171: Normalized crop classification confusion matrix (horizontal axis corresponds to the true label, whereas the vertical one to the predicted label)
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What derives from the classification results is that some crop types can be more easily
identified using EO-based deep learning methodologies (e.g. Maize, Cotton, Rice, Cereals).
Some other crops like rapeseed and tobacco are more challenging and sometimes they get
confused with other crops (e.g. cereals) that exhibit similar characteristics in terms of annual
cycles of growth and decline of vegetation (as “seen” by NDVI measurements).
For the assessment of “greening” compliance the trained models can be seen as the backbone
of the methodology. As in Trial 1, the farmers that could benefit from the methodology are
the ones holding parcels of >10ha that are eligible for checks for greening requirements
related to crop diversification. A traffic light system is employed to inform the farmers that
there could be a problem within his/her declarations. This means that:
a) if the confidence level of the classification result is >85% and the declared crop type
of the farmer was confirmed by the classification -> traffic light should be green
b) if the confidence level of the classification result is <85% and the declared crop type
of the farmer was confirmed by the classification -> traffic light should be yellow
c) if the declared crop type of the farmer was not confirmed by the classification -> traffic
light should be red
According to this approach, the farmer is more protected in order to receive the payment as
robust and reliable feedback is provided to him/her.
The following example effectively highlights the followed CAP support methodology and the
exploitation of the trained models.
The farmer holds a total arable area of more than 10ha, thus, the primary greening
requirement is met. In terms of crop diversification, the main crop type is Cereals with 6.88ha
in total. However, some issues have been identified and marked using the aforementioned
traffic light system.
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Table 9: Greening eligibility assessment using a traffic light system.
Crop group DataBio Assessment Traffic
Light
Area
Determined
(ha)
AP ID Declare
d
Detected Status Categorization
001 Cereals Cereals Assesse
d
Compliant 2.08
002 Cereals Cereals Assesse
d
Compliant 1.67
003 Maize Cereals Assesse
d
Not compliant 1.1
004 Maize Maize Assesse
d
Insufficient
evidence
1.46
005 Cereals Cereals Assesse
d
Insufficient
evidence
1.25
006 Cotton Cotton Assesse
d
Compliant 0.82
007 Cotton Cereals Assesse
d
Not compliant 0.73
008 Cereals Cereals Assesse
d
Compliant 1.88
Total 10.99
The farmer is notified for the issues (especially red indications are important as Cereals - the
main crop seems to cover more than 75% of the cultivated land) that puts at risk his/her
eligibility for greening compliance (the main crop may not cover more than 75% of the total
arable land), thus, contributing to raising awareness and allowing follow-up activities to be
taken.
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Figure 172: Greening eligibility assessment using a traffic light system (map projection example)
The final KPI measurements are collected. More specifically, with regular discussions with
GAIA EPICHEIREIN and its Thessaloniki FSC, final KPI measurements and feedback was
collected.
Trial 2 results
In Trial 2, the applied technologies and pipelines got even more mature and reached their
expected TRL. The key pilot stakeholders (i.e. the farmers and GAIA EPICHEIREIN that has a
supporting role in the crop type declaration process), continued (for a second year) to benefit
from the EO-based geospatial data analytics, thus, promoting the simplification and
improving the effectiveness of CAP. The pilot is fully aligned with the main concepts of the
new agricultural monitoring system and adopts a technology-driven traffic light methodology.
The traffic light system has proven to be a powerful tool in assessing the “greening” eligibility
conditions and informing the farmers about the assessment outcomes, thus, leading to fewer
errors and increased funds absorption.
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Components, datasets and pipelines
DataBio component deployment status
Component code
and name
Purpose for pilot Deployment status Component
location
C13.01
Neurocode (NP)
Neurocode allows
the creation of the
main pilot UIs in
order to be used
by the end-users
(GAIA
EPICHEIREIN) and
offering insights
regarding greening
compliance
deployed NP Servers
C13.02 GAIABus
DataSmart
Machine Learning
Subcomponent
(NP)
Supports EO data
preparation and
handling
functionalities
Supports multi-
temporal object-
based monitoring
and modelling and
crop type
identification
deployed NP Servers
C13.03 GAIABus
DataSmart Real-
time streaming
Subcomponent
(NP)
Real-time data
stream monitoring
for NP’s Gaiatrons
Infrastructure
installed in the
pilot sites
Real-time
validation of data
Real-time parsing
and cross-checking
deployed NP Servers
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C31.01 Neural
Network Suite
(CSEM)
Delivery of an
accurate machine
learning crop
identification
system to be used
for the detection
of crop
discrepancies
deployed CSEM’s
Servers
C04.02 – C04.04
Georocket,
Geotoolbox,
SmartVis3D
(Fraunhofer)
Back-end system
for Big Data
preparation,
handling fast
querying and
spatial
aggregations (data
courtesy of NP)
Front-end
application for
interactive data
visualization and
analytics
deployed Fraunhofer
Servers
Data Assets
Data Type Dataset Dataset original
source
Datase
t
locatio
n
Volum
e (GB)
Velocity
(GB/year)
EO products
in raster
format and
metadata
Dataset
comprised of
remote
sensing data
from the
Sentinel-2
optical
products (2
tiles)
ESA (Copernicus
Data)
GAIA
Cloud
(NP’s
servers
)
>2600 >850
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Sensor
measuremen
ts (numerical
data) and
metadata
(timestamps,
sensor id,
etc.)
Gaiasense
field. Dataset
composed of
measuremen
ts from NP’s
telemetric
IoT agro-
climate
stations
called
GAIATrons
for the pilot
area.
NEUROPUBLIC GAIA
Cloud
(NP’s
servers
)
Severa
l GBs
Configurable
collection and
transmission
rates for all
GAIATrons.
>20
GAIAtrons
fully
operational at
the area
collecting >
30MBs of data
per year each
with current
configuration
(measuremen
ts every 10
minutes)
Parcel
Geometries
(WKT),
alphanumeri
c parcel-
related data
and
metadata
(e.g.
timestamps)
Dataset
comprised of
agricultural
parcel
positions
expressed in
vectors
along with
several
attributes
and
extracted
multi-
temporal
vegetation
indices
associated
with them.
NEUROPUBLIC GAIA
Cloud
(NP’s
servers
)
Severa
l GBs
1 GB/year
The update
frequency
depends on
the velocity of
the incoming
EO data
streams and
the
assignment of
vegetation
indices
statistics to
each parcel.
Currently,
new Sentinel-
2 products are
available
every 5 days
approximatel
y and the
dataset is
updated in
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regular
intervals
Exploitation and Evaluation of pilot results
Pilot exploitation based on results
In the context of DataBio, NP has initiated a series of CAP Support activities for providing
supporting tools and services, in line with the commands of EC’s new agricultural monitoring
approach. This effort is expected to continue in the next years (contributing to the
sustainability of the projects outcomes) as part of another high-profile research project,
H2020 NIVA (https://www.niva4cap.eu/) where NP is a key partner and is close collaborating
with the Greek paying agency (OPEKEPE). This will allow evolving/further validating the
DataBio-enhanced services, so that they progressively become part of the suite of CAP
Support tools offered by GAIA EPICHEIREIN for aiding the crop declaration process.
From an implementation point of view, the quality of the provided services of NP greatly
benefited from the collaboration with leading technological partners like CSEM and
FRAUNHOFER, that specialize in the analysis of Big Data. Moreover, feedback from the end
users and lessons-learnt from DataBio’s pilot execution significantly fine-tuned and will
continue to shape the suite of dedicated tools and services, thus, facilitating their penetration
CAP Support line of business.
KPIs
KPI
short
name
KPI
description
Base
value
Targ
et
value
Measur
ed value
Unit
of
valu
e
Comment
C2.2_
1
Decrease in
false crop
type
declaration
s following
the
supporting
services vs
what
would be
expected
10 8 9.4 of
initial
declarat
ion
were
identifie
d as
potentia
lly
problem
atic
% A 9.4% of the initial farmer
declarations exhibited
potential errors based on
the followed
methodology. The farmers
were notified and received
follow-up information.
The offered advisory
services allow the farmers
holding parcels of >10ha
and more (prerequisite for
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based on
historical
data
the greening aid
application) to be
compliant to the greening
requirements in respect to
crop diversification, thus,
favoring a further
reduction to the
percentage of erroneous
declarations that threaten
funds absorption.
C2.2_
2
Accuracy in
crop type
identificati
on
No
prior
infor
mati
on
>80 98.5 % The overall accuracy of the
crop classification
methodology used in the
pilot reached 98.5%.
Respectively, precision
reached 99.1%, recall
94.6% and f1-measure
94.7%. Some crop types
seem to be more easily
identifiable (maize,
cotton, rice, cereals)
whereas others appear to
be more challenging
(rapeseed and tobacco)
C2.2_
3
Number of
crop types
covered
Initial
ly no
crops
were
being
cover
ed by
the
syste
m
7 7 crop
types
support
ed in the
greater
region
of
Thessal
oniki,
Greece
plain
num
ber
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16 Conclusion The document D1.3 describes the final status of agriculture pilots and concludes the Trial 2
including the performance indicators of the pilots. The final status of pilots also includes the
utilisation of Big Data datasets and the implementation status of DataBio
components/services defined in WP4 and WP5.
This document shows how all the individual pilots has reached their defined level of maturity
despite the different initial states of services and technologies and different level of services
integration before the start of Trial 1. Besides this, it is highlighted how, despite of different
territories and different thematic scope, the pilots have succeeded on the development of a
common approach to the problems solutions and a common focus of the use of Big Data
Technology and DataBio components.
DataBio results in agriculture are already actively used in new projects, such as NIVA16 or
DEMETER17, both of them working on the modernisation of European Agriculture.
16 The NIVA project (https://www.niva4cap.eu/project), developed by a consortium of 27 different partners including nine CAP (Common Agricultural Policy) Payment agencies, is the answer to the current discussion on the modernization of the CAP. Regarding this context, one of the main objectives of NIVA is to spread and obtain the maximum benefit from the ongoing digitization of the agricultural sector to reduce administrative burdens and to improve the sustainability and competitiveness of the sector. Through this digitization data-driven process, new potential for data use and reuse will emerge, thus, improved accessibility of CAP data as Big Data Sources. Those data sources have been proved a powerful tool for monitoring the societal benefits of agriculture towards rural development or climate change mitigation, therefore an improved access to them will endorse the current process and will define new and promising ways of use. 17 The DEMETER project (http://h2020-demeter.eu/) objective is to support farmers and cooperatives with their decisions regarding the control of their production and how they will manage Farming Information Systems and associated technologies more efficiently. Hence, fully aligned with DataBio results, a key objective of DEMETER is by demonstrating the impact of digital innovation and interoperable platforms to allow the farmers to increase the possible combination of tools from different suppliers or providers.