igreen: co-creative mobile services in agriculture - a knowledge management perspective

© German Research Center for Artificial Intelligence - [email protected] 2012 -

Andreas Dengel


Agenda

Some words about the world‘s food situations

The iGreen project

Agricultural knowledge management – two perspectives

Consolidating knowledge and decision support services

Summary and conclusions

Crop forecasting

Logistic planning

Soil quality map adjustment


Agriculture must provide food for an extra 2 billion people but 80% of extra agricultural food can be provided only through yield increase!

Source: (1) UN, World population to exceed 9 billion by 2050. http://www.un.org/esa/population/publications/wpp2008/pressrelease.pdf, 2008 (2) N. Alexandratos, J. Bruinsma, G. Boedeker, J. Schmidhuber, S. Broca, P. Shettym, and MG Ottaviani. World agriculture: towards 2030/2050. Interim Report. Prospects For Food, Nutrition, Agriculture and Major Commodity Groups, 2006.

9 billion people by 2050!

Extra agricultural food production

By increasing yield By increasing arable land

100%

≤20% ≥80%

Too many factors High complexity!

?

2010 2050

+43%

World demand for cereal (bn tonnes2)

2,1

3,0

-‐0.2%

2050 2010

Amount of arable land in developed countries

(mn hectares)

625 575


IT can help to improve and economize methods, procedures and agricultural technologies to produce more efficiently and save resources

¨  Analysis of high-‐resolution images from space: Controlled specialized satellites take pictures of agricultural region all over the world

¨  Field robots will take over some agricultural tasks: Equipped vehicles (not only for harvesting) are staffed with sensors to measure the state of crop and soil

¨  Precision farming: Linked and controlled by GPS, seed, fertilizer and pesticides are yielded exactly where they are needed

¨  Digital soil quality maps: Continuous measure lead to updated guidelines helping to plan the supply of nutrients or predict harvest


What are the

ideas of the iGreen project?


Consul'ng services Interests of the social community Interests of the agricultural sector

Das Bildelement mit der Beziehungs-ID rId7 wurde in der Datei nicht gefunden. Farmers / agricultural service

supply agency

iGreen builds on an alliance of 23 partners from science, business and public institutions

private public

Applica4on Management

EPP

e.v .

(na4onwide)

iis

(interna'onal) (na'onal) (regional)

Connec4vity


iGreen strikes a new path in public-‐private knowledge management having a focus in agriculture

¨  The goal of iGreen is to develop location-‐bases services and knowledge sharing networks for combining distributed, heterogeneous public as well as private information sources

¨  Built on that, we aim at the development of mobile decision assistants using Web services for a decentralized support of

energy-‐efficient, economic, environmental-‐adapted and collaboratively-‐organized

production and planning processes

¨  There are many high-‐potential application fields for the iGreen platform, such as agriculture, forestry, water supply and distribution, urban development and landscaping, or nature conservation

¨  in iGreen we exemplary focus on crop production


In crop production there is whole bunch of relevant questions having a space-‐time relationship that have to be answered for making decisions

When should we apply what kind of fertilizer and to which dose?

Which kind of plant species should we cultivate at what location?

How much pesticides should we apply at

what time?

When is the best time for the harvest?

What market prices may we expect?


Public country-‐wide institutions in Germany act as competence centers and provide best-‐practice advice about crop production for farmers

Information about how to combat pest and plant diseases

(best time, conditions and strategies)

Competence Center

Compendium about Best-‐Practice Crop Protection

Regional Data About Fields and Lots

Data Base about Crop Experiment Results

News about Market Development

Support for Data Management and Model Generation

Advise in Grade and Seed Selection

Consulting about Fertilizer Combination and Use

Tools for Prognoses and Statistical Data Analysis


Some examples about the services from the competence centers

Competence Center

Exposition Regional Factor

Gradient

++ =Risc Map

2009 2011

Pest Immigration

Weather Station Information (Point⇒Zone)

⇒ ⇒

Data Interpolation Methods (MR, Kriging, spline, ....)


?

Yield depends on many factors – Experts and Decision Support Systems help farmers to optimize yields

Ecological factors

•  Soil quality • Weather condition (rainfall)

•  Sun exposition

Social factors

•  Transfer of skills and roles

•  Security of land tenure

• Access to land

Economical factors

•  Fuel, fertilizers, pesticides prices

•  Product selling prices (Market)

• …

Political factors

• Agricultural subventions

• Quotas

• Other restrictions

Yield

+ Ecological factors + Economical factors + Political factors + Social factors + Model(s) ________________ Optimized Yield

So how to get all of these factors combined?


iGreen profits from recent technological advances that, in the intended combination, will strongly influence agricultural progresses

¨  Public information as well as product and expert knowledge are increasingly digitized and available via the internet, such as different kinds of maps or weather data

Soil Quality Estimation

Methods

Thematical Maps

¨  Accessing the internet is possible from almost all locations via mobile devices

¨  GPS devices for positioning are easy to buy or are even part of a mobile phone

¨  Agricultural engineering provides more and more software interfaces for the automatic situation-‐adaptive control (on-‐board terminals, sensor technology, …

So we are going to establish a Service and Knowledge Network


Competence Center

Public

Private

The iGreen Planning System combines knowledge subjects and information objects for a participative public-‐private knowledge management

Data Provision Geo Catalogue Agro Databases

Feed

back

Feedback Prognoses

Global Optimization

Local Optimization Free Service for Farmers

Planning System

Farmers Location-‐based Decision Assistance


Flexible Data Management is a challenge

¨  The only condition is individual data ownership

-‐ local filing combined with controlled interchange within the iGreen network

Before you start talking about sensor data, please provide me first an option to file, manage

and employ the data

Farmer and Contractor Marx

“ “


Public-‐private

knowledge management


Knowledge Management is a process for improving organizational capabilities by better use of individual and collective knowledge resources

1.   Knowledge Goals: Define all capabilities an organization should build on

2.   Knowledge Identification: Identify internal and external knowledge of the organization

3.   Knowledge Acquisition: Critical capabilities must be bought or otherwise obtained

4.   Knowledge Development: Produce new internal and external knowledge (individual & collective level)

5.   Knowledge Dissemination: Define who should know what and at what level of detail, and how the organization can support this distribution process

6.   Knowledge Utilization: Productive deployment of organizational knowledge in the business/production process of the organization

7.   Knowledge Preservation: Identify valuable knowledge, store it, and regularly integrate it into the organizational knowledge base

8.  Knowledge Controlling: Compare initial knowledge goals with results of organizational knowledge magt.

Source: G.J.B. Probst, S. Raub, and K. Romhardt. Wissen managen. Gabler, 1997. ISBN 3409193170.

Definition

2

Identify knowledge

4

Develop knowledge

5 Distribute knowledge

6 Use knowledge

7 Preserve knowledge

8 Control knowledge

3

Acquire knowledge

1

Define knowledge

Goals

Feedback Knowledge!Management!

Cycle!


In terms of knowledge management, both experts and farmers have their own knowledge management cycle

Increase Yields

Develop DSS

2

Identify knowledge

4

Develop knowledge


6 Use knowledge


8 Control knowledge

3

Acquire knowledge

1

Define knowledge

Goals

Feedback

Decision Support System (DSS)

Define knowledge

Goals

Disseminate knowledge 5

3 Acquire knowledge

4

2

6

1

8

7

Use knowledge

Preserve knowledge

Control knowledge

Identify knowledge

Develop knowledge

Feedback



Define knowledge

Goals


3 Acquire knowledge

4

2

6

1

8

7

Use knowledge

Preserve knowledge

Identify knowledge

Develop knowledge

2

Identify knowledge

4

Develop knowledge


6 Use knowledge


8 Control knowledge

3

Acquire knowledge

1

Define knowledge

Goals

Feedback


Increase Yields

Develop DSS

Feedback

Control knowledge



Define knowledge

Goals


3 Acquire knowledge

4

1 Develop

knowledge

4

Develop knowledge

6 Use knowledge

3

Acquire knowledge

1

Define knowledge

Goals


Increase Yields

Develop DSS


Increase Yields


Define knowledge

Goals


3 Acquire knowledge

4

1 Develop

knowledge

4

Develop knowledge

6 Use knowledge

3

Acquire knowledge

1

Define knowledge

Goals

Soil Type

SSIISSLsLLLTTMo

Soil quality map from the 1950’s (Low definition and outdated)

No better decision support !

No better map material!

Not Possible!

No better advice


Develop DSS


Source: R. Grisso, M. Alley, and P. McCellan. Precision farming tools: yield monitor. Precision Farming, pages 442–502, 2003.

GPS Technology combined with agricultural sensors to measure the yield

Available agricultural technology allows farmers to derive accurate and up to date soil quality maps

Mass Flow Sensor Moisture Sensor

GPS Receiver

Task Computer User Interface

Soil quality map from the 1950’s : •  Outdated •  Low resolution

Soil quality map from precision ag.: •  Up to date •  High resolution


Increase Yields

A collaborative knowledge management approach can lead to better agricultural decision support

Define knowledge

Goals


3 Acquire knowledge

4

1 Develop

knowledge

4

Develop knowledge

6 Use knowledge

3

Acquire knowledge

1

Define knowledge

Goals

Better Decision Support

Better DSS

Possible

Yield in t/ha

0 -‐22,01 – 44.01 – 66.01 – 8>8

Yield maps (High definition & up-‐to-‐date) Better

map material!

Win-‐Win Situation

Develop DSS


Farmers can collaboratively contribute to the acquisition of better geo-‐data and get in return better decision support

Partial collaboration: Data is only shared with the experts

Full collaboration: Data is shared within the community

Geo-‐Data

Information Supply


Scenario planning allows

crop forecast


The collaborative contribution was implemented in applications for biomass and logistics planning

Biomass Planning

Goal • Computing the optimal biomass yield based on: –  list of fields –  production plan –  soil quality – weather conditions

Resources • Experts provide models and weather data • Farmers provide accurate soil quality maps using precision agriculture and a production plan

Approach • Soils quality maps provided by a farmer are used to improve results but remain confidential

• Models are improved and benefit the whole community

Examples of Applications and Evaluations

Partial collaboration!


Tasks and their dependencies of Biomass-‐Yield-‐Models (BYM) are transformed into a scientific workflow

n  Advantages of scientific workflows: •  Data flow approach based on visual programming •  Modularity and reusability •  Provenance information to better interpret results and

debug errors

Computational steps for scientific simulations or data-‐analysis steps

Source: B. Ludäscher, I. Altintas, C. Berkley, D. Higgins, E. Jaeger, M. Jones, E.A. Lee, J. Tao, and Y. Zhao. Scientific workflow management and the Kepler system: Research articles. Concurr. Comput. : Pract. Exper., 18:1039-‐1065, August 2006. ISSN 1532-‐0626. doi: 10.1002/cpe.v18:10.


Based on various public sources we have developed a Crop Forecast System aiming at supporting the farmer in making decisions

* Zentralstelle der Länder für EDV-‐gestützte Entscheidungshilfen und Programme im Pflanzenschutz

Rain

Soil Quality Data

Soil Quality

Intermediate Result

Crop Forecast Result Map

Result Table

Result Chart

Agricltural Crop Land Online Rheinland-‐Pfalz (FLOrlp)

Region

Biomass Yield Models (BYM)

BYM

Filter

Pre-‐given Goal


A Mashup of maps and tabular information provides a intuitive platform for biomass planning

Results for fields

Results for subfields

Ecological or conventional yield

3 rainfall scenarios (dry, normal, wet)

Excel and Google Earth export


In Google Earth subfield-‐related information may be accessed via any mobile device


© [email protected] - 2008

Exporting the information to an Excel spreadsheet (with Macros) supports realizing a production plan


We have tested the forecast on a farm by evaluating four tours*

512 550

667

459

1.215

443

651

-‐2%

-‐19%

-‐15%

-‐10%

Tour 4 Tour 3 Tour 2 Tour 1

Biomass (t)

Real Forecast

Weed infestations

One field not be harvested

* 16 fields to be harvested; avg. dist. betw. field and silo: 9.5 km; average exploitable acreage: 2.44 ha


Logistic planning allows to remarkably save fuel


The collaborative contribution was implemented in applications for biomass and logistics planning

Biomass Planning

Goal • Computing the optimal biomass yield based on: –  list of fields –  production plan –  soil quality – weather conditions

Resources • Experts provide models and weather data • Farmers provide accurate soil quality maps using precision agriculture and a production plan

Approach • Soils quality maps provided by a farmer are used to improve results but remain confidential


Examples of Applications and Evaluations

Logistics Planning

• Computing the optimal logistics plan, i.e., route and costs for: –  harvester(s) –  tractor(s) with trailers

• Experts provide models • Farmers provide accurate GPS-‐Tracks and meta-‐information about their routes using precision agriculture and number of harvesters and tractors available

• GPS-‐Tracks and meta-‐information provided by a farmer are sanitized and shared with the whole community


Full collaboration!


Current available route guidance systems do not provide information about road narrowing nor bridge weights limits


This way, we also developed a first collaborative open-‐source routing system for utilities vehicles (CRUV)

Vehicle type (car, lorry, tractor)

-‐ Road types: Motorway, highway, country road, field path with priorities (preferred, normal, avoid, forbidden)

-‐ Tunnels and bridges with priorities

Vehicle length, weight, height, width

Rules based on user meta-‐information stored in Open Street Maps


CRUV: 186 km Map24: 186 km

Compared to Map24, there is no difference in quality in traditional route planning


GoogleMaps: 1,5 km CRUV: 2,8 km

However, CRUV reveals its advantages when including the additional information, e.g. with bridges and maximal weight allowed

Google Maps does not support the kind of query!


A logistic planer computes transportation costs based on the results from the CRUV and biomass planner

Route between POI based on driving distance Slope (of the road)

Point of interest (ex: Biogas plant)

List of Fields (< d): -‐ Size -‐ Driving distance -‐ Crop -‐ Yield -‐ Transportation costs

Field repartition with respect to the driving distance

Max. driving distance d between field and POI

Overview with Biomass and costs


We have tested the forecast on a farm by evaluating four tours*

512 550

667

459

1.215

443

651

-‐2%

-‐19%

-‐15%

-‐10%

Tour 4 Tour 3 Tour 2 Tour 1

Biomass (t)

Real Forecast

Weed infestations

One field not be harvested

* 16 fields to be harvested; avg. dist. betw. field and silo: 9.5 km; average exploitable acreage: 2.44 ha

6.714

14.077

5.086

6.320 6.542

8.003

6.816

Tour 2 Tour 1

+8% +57%

+4%

-‐3%

Tour 4 Tour 3

Real Forecast

Transportation costs (EUR)

Higher fuel consumption due to dry soil


The goals of iGreen are very relevant for many social systems

¨  iGreen follows an approach leading to more accurate and geographically differentiated agricultural forecast by

-‐  leveraging existing farmers GPS-‐based precision agriculture technology

-‐  collaboratively acquiring accurate, high-‐resolution, and up-‐to-‐dare geo-‐data

-‐  allowing public agricultural institutions, in return, to use this geo-‐data to provide farmers with better decision support tools

¨  Biomass Planner

-‐  A decision support system relying on this approach to provide better biomass planning

-‐  Both the biomass model and soil quality maps are improved but no data is shared with the community. Only the improved model benefits the community

¨  Logistic Planner -‐  A decision support system relying on this approach to

provide better logistics planning

-‐  Both the model and the geo-‐data are shared with the community


Moreover,

existing soil quality maps may be continuously improved

So, we are going to close th

e!

Knowledge Management!

Cycle!


Remember that forecast is based on the data coming from existing soil quality maps!


40

33 44

53

61

Digital soil quality maps provided by state government allow farmers to get an overview about the quality of their fields

Additional information may be accessed via the lot No.


Agricultural engineering provides relevant location-‐based data

Recording of location, crop, humidity, and fuel consumption:

Humidity Sensor

Melt Flow Sensor


CROP (t/ha) to from

Location-‐based recording of crop data leads to an up-‐date of soil quality maps

Recorded on 29-‐09-‐2008 Average Value 12,23 t/ha Try Solids 100,40 t Humidity 73,55 %

HISTOGRAM


Decision support in crop production provides important contributions for increasing efficiency, saving resources, and reducing environmental impact

¨  Winter wheat is the most important crop in Germany having a total acreage of 3 Mio hectares. Experts estimate that each hectare of winter wheat requires about 200 kg of nitrate per year. Nitrate production causes about 50% of energy consumption in crop production

¨  The German agriculture may …

... lead to possible energy saving potential of 30.000 tons of nitrate per year

... avoid climate-‐relevant emissions of 257,000 tons of CO2 annually

... reduces the amount of nitrate-‐containing nutriments in bodies of water

¨  Using location-‐adaptive fertilizer dispersion for wheat production based on improved soil quality maps , it was possible to save ca. 5% of nitrates without reducing the amount of crop

Exemplary result (10 farmers in the north of the German state Rhineland-‐Palatinate):


The goals of iGreen are very relevant for many social systems

¨  Food production is not a task just of agriculture, but a central aim of the global society

¨  In case of crisis, food security has to be guaranteed by both, federal and private organizations (this is part of the food precaution law and food ensuring law in Germany)

Long-‐term Public-‐Private Partnerships are of major importance

¨  iGreen use cases and demonstrators prove the new options based on real application scenarios

¨  iGreen SDK provides general and fundamental components as open source

¨  iGreen documents (processes, scenarios, interfaces) provide excellent guidelines, e.g. who communicates with whom ,why, and with what tools


Thank you, questions are welcome!

igreen: co-creative mobile services in agriculture - a knowledge management perspective

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