LemnaTecTHE PHENOTYPING COMPANY

SINCE 1998

Sensor-based phenotyping technology facilitates science and breeding

Images describe phenotypes

Quantitative or qualitative data?

Phenome

Metabolome

Proteome

Transcriptome

Genome

Still many studies give only

qualitative data for phenotypic

properties, while underlying

biochemistry and molecular

biology of course is given in a

quantitative manner.

Phenotyping software

Translating images into numerical data

Do we need numerical phenotypic data?

Regardless how you modify the

crop, you are interested in the

resulting phenotypes, and you

need to quantify the success of

modification!

Cabbage Taraxacum Rapeseed Rice Wheat Tomato

Tomato-roots Pepper Maize Ficus Cabbage Cucumber

Cotton Tobacco Sorghum Miscanthus Sugarbeet Grass

Componets of phenotyping

Plants (biological objects) Environment

Experimental setupSensors, measurement

platforms, software

Phenotyping result

LemnaTec OS – advanced phenotyping software

LemnaControl

Control of hardware plant carriers, sensors, watering, spraying

API for integration of a wide range of sensors

Programmable interface for non-LemnaTec equipment

Centralised data acquisition

LemnaGrid

Graphical programming of image analysis

Library of image processing algorithms including hyperspectral data

API for integration of third party image processing software

LemnaBase

Database management

Open access to databases with full documentation

Graphical display of images, data and analysis

Metadata

Lemna-R

Easy access to all data with plotting function

Integrates seamlessly with R statistics

Configurable data processing functions

Visualisation of data and images

Drag & drop data processing

Plants surface and shape determination with LemnaGrid software

Digital phenotyping – multiple sensors deliver comprehensive data sets

SENSOR MEASURED PARAMETERS DERIVED BIOLOGICAL INFORMATION

VIS cameraDimensions ("digital biomass"),

geometry, colourGrowth, biomass, development, stress

Laser scanner 3D point cloud Growth, geometry, organ-resolved information

Hyperspectral camera Spectrally resolved imagesBiomass, physiology, pigments, water status,

stress, diseases, vegetation indices

PSII camera Chlorophyll fluorescence Photosynthetic parameters

IR camera Surface heat emission Temperatures, transpiration

NIR camera Reflectance due to water content Water status

Fluo-camera Fluorescence signalsChlorophyll, senescence, fluorescent pigments,

biomarkers

Mahlein, Anne-Katrin (2016): Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. In: Plant Disease 100 (2), S. 241–251. DOI: 10.1094/PDIS-03-15-0340-FE.

Multi-level phenotyping – example plant diseases

Laboratory Systems

PhenoBox Entry level bench-top instrument Small footprint, low cost

Lab Scanalyzer Advanced bench-top instrument Wide range of sensors Top and side view

HTS Lab Scanalyzer Reproducible screening High throughput Automation options High precision positioning

Common applications Seedlings In-vitro germination tests Population screens Gene functions Herbicide, insecticide tests Ecotoxicology – duckweed test Feeding and motility tests with insects, mites etc. Microbial colony counting

Feeding tests

HT-screening for leaf eating organisms

feeding assays

resistance screens

organism sizes

mortality assessment

Saran, Raj K.; Ziegler, Melissa; Kudlie, Sara; Harrison, Danielle; Leva, David M.; Scherer, Clay; Coffelt, Mark A. (2014): Behavioral Effects and Tunneling Responses of Eastern Subterranean Termites (Isoptera: Rhinotermitidae) Exposed to Chlorantraniliprole-Treated Soils. In: Journal of Economic Entomology107 (5), S. 1878–1889. DOI: 10.1603/EC11393.

Fruit screening

Laser Scanner – 3D point cloud

Dornbusch, T. et al. (2014) Differentially Phased Leaf Growth and Movements in Arabidopsis Depend on Coordinated Circadian and Light Regulation. The Plant Cell 26, 3911–39212 Dornbusch, T. et al. (2012) Measuring the diurnal pattern of leaf hyponasty and growth in Arabidopsis - a novel phenotyping approach using laserscanning. Functional Plant Biology 39, 860

Greenhouse Scanalyzer System

Automated indoor phenotyping Complete solutions Modular construction Fully configurable Robust and reliable

Features Multiple imaging for 3D calculations Weighing and watering Plant density optimization Plant tracking

Multiple Sensors

Plant size, morphology, and colour

Maize top and side view

RGB camera

Plant and soil water status

Near infrared light (NIR) reflectance relates to tissue water content

measuring water distribution within plants or soil and dynamic changes in time

wheat dried down over 16 h at elevated temperature

0h

8h

4h

16h

0h 2h 4h 6h 8h

Soil water contentmonitoring

Fluorescence imaging

Fluorescence signals

Senescence, autofluorescence

Hairmansis A, Berger B, Tester M, Roy SJ (2014) Image-based phenotyping for non-destructive screening of different salinity tolerance traits in rice. Rice 7: 16

Australian Plant Phenomics Facility - 2009

Cereals – response to water limitation

Measurement – parameters – information – knowledgeImages – plant area data – biomass calculation – QTL discovery

Field Scanalyzer System

Automated outdoor phenotyping Modular construction Fully configurable Comprehensive datasets

Repeatable measurements High precision positioning Fully automated 24 x 7 Robust and weatherproof

Multiple sensors

Field Scanalyzer – Arizona, USA

Enviormental Sensors – „ecotyping“

• CO2 Sensor

• NDVI Sensor

• Active Reflectance Sensor (Crop Circle)

• PAR Sensor

• Color Sensor

• General Enviormental Sensors

• Rain

• Wind

• Light

Phenotyping sensors

• 2x 9MP RGB Camera• Mounted on a flexible base plate• Cooled Housing

• FLIR thermal camera

• PSII camera (Kautsky effect)

• Laser scanners - special development by Fraunhofer IIS• 0.6m Scan width• 1.5m Scan depth (adjustable)• 0.25mm point to point distance• 2x Side looking with different setup

• Hyperspectral cameras

Visible light images – ear counting

Thermal imaging – canopy emission of heat radiation

PSII imaging – chlorophyll fluorescence parameters

F0 – dark adapted

Fm – dark adapted

Maximum quantum efficiency of PSII: (Fm - F0) / Fm

(Fm - F0) / Fm

1

0

3D Laserscanning – canopy, plant, and organ size and morphology

Hyperspectral data and vegetation indices

Modified Chlorophyll absorption in Reflectance index (MCARI)

Modified Chlorophyll Absorption in Reflectance Index (MCARI1)

Soil adjusted vegetation indices (XSAVI)

Optimised Soil Adjusted Vegetation Index (OSAVI)

Gitelson and Merzlyak Indiex1

Gitelson and Merzlyak Indicex2

Red Edge Normalized Difference Vegetation Index NDVI705

Modified Red Edge Simple Ratio Index

Modified Red Edge Normalized Difference Vegetation Index

Greenness Index

Vogelmann Indicex1

Vogelmann Index2

Vogelmann Index3

Transformed CAR Index (TCARI)

Simple Ratio Pigment Index (SRPI)

Normalised Phaeophytinization Index NPQI

Carotenoid Reflectance Index 1

Carotenoid Reflectance Index 2

Anthocyanin Reflectance Index 1

Anthocyanin Reflectance Index 2

Plant Senescence Reflectance Index

Photochemical Reflectance Index (PRI)

Nitrogen related index NRI1510

Nitrogen related index NRI850

Normalized Difference Nitrogen Index

Normalized Pigment Chlorophyll Index (NPCI)

Carter Index1

Carter Index2

Lichtenthaler Index1

Lichtenthaler Index2

Structure Insensitive Pigment Index (SIPI)

NVDI Turf Colorimeter

Water Band Index

Water index (Thiel, Rath , Ruckelshausen)

Normalized Difference Water Index

Moisture Stress Index

Normalized Difference Infrared Index

Desease-Water Stress Index 1

Desease-Water Stress Index 2

Desease-Water Stress Index 3

Desease-Water Stress Index 4

Desease-Water Stress Index 5

Leaf structure index R1110/R810

Normalized Difference Lignin Index

Cellulose Absorption Index

extended VNIR

VNIR

normal NIR

SWIR

250 500 750 1000 1250 1500 1750 2000 2250 2500

Field phenotyping

From the gantry system towards vehicles and flying platforms

Seed and germination assessment

Automated Germination System

ID seeds, germination time, speed, root length

Seedling on agar plates

Measuring roots:• Root length• Root thickness• Root branching• Root types• Root system architecture

Measuring shoots• Area• Leaf count• Leaf dimensions• Leaf shape• Colour

Custom designed robotics – plate imaging

Root dilemma

Root visibility

“Natural” cultivation

• Comparable to other growth situations

Root visibility

Artificial cultivation

• Specific for experiment

Phenotyping is multidisciplinary

Plant science

Information science

Machinelearning

Breeding

Plant health

Agronomy

Toxicology

Data scienceAutomation

Engineering

Sensor technology

Computer vision

Optics

Environment Greenhouses, growth rooms

Climate

Some users of LemnaTec phenotyping technology

Phenotyping needs networks and international initiatives

Pre-conference satelliteevent, 24th and 25th October

HOTEL NHOW BERLINWednesday | Oct 26th 2016 | 9:00until Thursday | Oct 27th 2016 | 18:00

IPPN Symposium at CIMMYTEl Batan, Mexico13.-15.12.2016

Phenotyping conferences 2016