LemnaTec talk at Redbio Conference 2016

1. LemnaTec
THE PHENOTYPING COMPANY
SINCE 1998
Sensor-based
phenotyping technology
facilitates science and
breeding

2. Images describe phenotypes

3. 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.

4. Phenotyping software
Translating images into numerical data

5. 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!

6. Cabbage Taraxacum Rapeseed Rice Wheat Tomato
Tomato-roots Pepper Maize Ficus Cabbage Cucumber
Cotton Tobacco Sorghum Miscanthus Sugarbeet Grass

7. Componets of phenotyping
Plants (biological objects) Environment
Experimental setup
Sensors, measurement
platforms, software
Phenotyping
result

8. 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

9. Drag & drop data processing
Plants surface and shape determination with LemnaGrid software

10. Digital phenotyping – multiple sensors deliver comprehensive data sets
SENSOR MEASURED PARAMETERS DERIVED BIOLOGICAL INFORMATION
VIS camera
Dimensions ("digital biomass"),
geometry, colour
Growth, biomass, development, stress
Laser scanner 3D point cloud Growth, geometry, organ-resolved information
Hyperspectral camera Spectrally resolved images
Biomass, 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 signals
Chlorophyll, senescence, fluorescent pigments,
biomarkers

11. 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

12. 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

13. 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 Entomology
107 (5), S. 1878–1889. DOI: 10.1603/EC11393.

14. Fruit screening

15. 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 laser
scanning. Functional Plant Biology 39, 860

16. 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

17. Plant size, morphology, and colour
Maize top and side view
RGB camera

18. 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 content
monitoring

19. 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

20. Australian Plant Phenomics Facility - 2009

21. Cereals – response to water limitation
Measurement – parameters – information – knowledge
Images – plant area data – biomass calculation – QTL discovery

22. 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

23. Field Scanalyzer – Arizona, USA

24. Enviormental Sensors – „ecotyping“
• CO2 Sensor
• NDVI Sensor
• Active Reflectance Sensor (Crop Circle)
• PAR Sensor
• Color Sensor
• General Enviormental Sensors
• Rain
• Wind
• Light

25. 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

26. Visible light images – ear counting

27. Thermal imaging – canopy emission of heat radiation

28. PSII imaging – chlorophyll fluorescence parameters
F0 – dark adapted
Fm – dark adapted
Maximum quantum efficiency of PSII: (Fm - F0) / Fm
(Fm - F0) / Fm
1
0

29. 3D Laserscanning – canopy, plant, and organ size and morphology

30. 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

31. Field phenotyping
 From the gantry system towards vehicles and flying platforms

32. Seed and germination assessment
Automated Germination System

33. ID seeds, germination time, speed, root length

34. 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

35. Custom designed robotics – plate imaging

36. Root dilemma
Root
visibility
“Natural”
cultivation
• Comparable to other
growth situations
Root
visibility
Artificial
cultivation
• Specific for
experiment

37. Phenotyping is multidisciplinary
Plant science
Information
science
Machine
learning
Breeding
Plant
health
Agronomy
Toxicology
Data
scienceAutomation
Engineering
Sensor
technology
Computer
vision
Optics
EnvironmentGreenhouses,
growth rooms
Climate

38. Some users of LemnaTec phenotyping technology

39. Phenotyping needs networks and international initiatives

40. Pre-conference satellite
event, 24th and 25th
October
HOTEL NHOW BERLIN
Wednesday | Oct 26th 2016 | 9:00
until Thursday | Oct 27th 2016 | 18:00
IPPN Symposium at
CIMMYT
El Batan, Mexico
13.-15.12.2016
Phenotyping conferences 2016