Digital Phenotyping of field crops under field conditions

Digital Phenotyping of field crops under field conditions

Stefan Schwartz Business Development stefan.schwartz@lemnatec.com

LemnaTec  founded May 1998 in Aachen, Germany

 interdisciplinary team (biology, physics, engineering)  14 years of experience with image based biological measurement (phenotyping, ecotoxiology)

 in-house development of image processing software  in-house development of hardware

Speaker: Stefan Schwartz

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LemnaTec

Speaker: Stefan Schwartz

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LemnaTec

200

Speaker: Stefan Schwartz

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The human eye

How many legs does this elephant have?

The human eye

Count the number of black dots‌

The human eye The circles in the centers‌

‌are exactly the same size

scanalyzerField

Field Phenotyping System

scanalyzerField

Speaker: Stefan Schwartz

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scanalyzerField – movable arm  System width up to 10 m  System length up to 40 m  System height up to 6 m

Speaker: Stefan Schwartz

 Customised to application  Based on concrete foundation

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scanalyzerField – movable arm  Support for movable cameras, scanners and sensors  Height range X-y m  Movement speed  Precision

Speaker: Stefan Schwartz

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scanalyzerField – movable arm  Key element sensor platform

 High precision movement of the camera and sensor unit (reproducibility 5 mm)  up to 4 top cameras and scanners

 Movable arm to mount side cameras or adjustable scanners  Optional addition of environmental monitoring sensors  Option for irrigation  Maximum load 60 kg Speaker: Stefan Schwartz

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scanalyzerField – Cameras  Cameras are shielded against dust and heat  Top cameras in different wavelength  Multiple side images are possible for different height levels  Height scanning Speaker: Stefan Schwartz

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scanalyzerField HTS – Crops Wide application range for nearly all crops:  Small trees even in containers  High density crops to be imaged from top only (cereals, rape)

 Crops growing in rows or plots with intermediate space (corn, potatoes, beets, legumes, pulses)  All field crops in natural soil or containers Speaker: Stefan Schwartz

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Phenotyping the Data

Interpreting the numbers

The experiment  Different species have been grown on the PhenoFab system in Wageningen  Each species had 10 different genotypes in a fivefold replicat  The plants have been screened daily for 35 days with 5 images each day ( 4 side images and a top image )  All images have been analysed to extract projected area

Speaker: Stefan Schwartz

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Overview

Speaker: Stefan Schwartz

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Growth Movies

Speaker: Stefan Schwartz

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Growth Plot

Significant difference after the first 5 days

LemnaTec scanalyzer Field

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Growth Plot Begin of drought stress

Increased amount of water

LemnaTec scanalyzer Field

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Correlation to manual measurement  Last day of experiment plants have been harvested  Height and fresh weight was measured manually  Correlated to images of last day before harvesting

R²=0.9563 Speaker: Stefan Schwartz

R²=0.9857 Slide 21

Growth Modeling  Each replicate growth curve has been averaged over the different views and then been modeled  Woods incomplete gamma function (3 parameters) đ?‘Ś đ?‘Ą = đ?‘Ž đ?‘Ą đ?‘? đ?‘’ −đ?‘?đ?‘Ą

đ?‘Ž: inital size đ?‘?: inital growth đ?‘?: secondary growth

 An average parameter with confidence intervals was then created for each genotype out of the ten replicats for side and top view  Result: 6 Parameters for each Genotype Speaker: Stefan Schwartz

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Plant Growth Fingerprint Tobacco - BAAA Initial growth side -2 0 2

Secondary growth top x40

4

Initial size side

6 8 10

Initial size top

Secondary growth side x40

Lower 95% confidence limit

Initial growth top

Mean value Upper 95% confidence limit

Speaker: Stefan Schwartz

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Comparing different Genotypes BBAA

BBAA

BCAA

-2

-2

-2

-2

-2

3

3

3

3

3

8

8

8

8

8

BFAA

BGAA

BHAA

BDAA

BEAA

BJAA

BIAA

-2

-2

-2

-2

-2

3

3

3

3

3

8

8

8

8

8

High similarity Speaker: Stefan Schwartz

Big difference Slide 24

Comparison Metric  

The 6 parameters that describe can be considered as a 6 dimensional vector that describes growth behaviour Similarities between those vectors can be expressed by an angle measure such as <đ?&#x2018;&#x17D; ,đ?&#x2018;&#x17D; > đ?&#x153;&#x192; = đ?&#x2018;&#x17D; 1 đ?&#x2018;&#x17D;2 1

ď&#x201A;§

2

A matrix that compares all genotypes to each other can then be created giving a similarity of the matching phenotypic fingerprint between 0 and 1 (1 maximum similiraty)

BAAA BBAA BCAA BDAA BEAA BFAA BGAA BHAA BIAA BJAA

BAAA BBAA BCAA BDAA BEAA 100,0% 86,9% 88,5% 77,7% 85,1% 100,0% 99,0% 81,6% 97,7% 100,0% 88,6% 96,1% 100,0% 74,1% 100,0%

Speaker: Stefan Schwartz

BFAA BGAA BHAA 91,0% 82,5% 91,0% 92,1% 96,9% 96,9% 96,3% 98,8% 99,1% 93,5% 92,4% 92,2% 89,8% 92,0% 92,1% 100,0% 95,2% 97,7% 100,0% 98,4% 100,0%

BIAA 87,3% 99,0% 99,1% 86,6% 94,0% 93,4% 98,3% 98,6% 100,0%

BJAA 87,2% 89,9% 87,1% 59,1% 95,4% 83,0% 79,1% 83,2% 84,7% 100,0%

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Comparison Metric BAAA BBAA BCAA BDAA BEAA BFAA BGAA BHAA BIAA BJAA

BAAA BBAA BCAA BDAA BEAA 100,0% 86,9% 88,5% 77,7% 85,1% 100,0% 99,0% 81,6% 97,7% 100,0% 88,6% 96,1% 100,0% 74,1% 100,0%

BFAA BGAA BHAA 91,0% 82,5% 91,0% 92,1% 96,9% 96,9% 96,3% 98,8% 99,1% 93,5% 92,4% 92,2% 89,8% 92,0% 92,1% 100,0% 95,2% 97,7% 100,0% 98,4% 100,0%

-2

-2 3 8

BBAA BCAA BGAA BHAA

BIAA

3 8

BIAA 87,3% 99,0% 99,1% 86,6% 94,0% 93,4% 98,3% 98,6% 100,0%

BJAA 87,2% 89,9% 87,1% 59,1% 95,4% 83,0% 79,1% 83,2% 84,7% 100,0%

BAAA BBAA BCAA BDAA BEAA BFAA BGAA

BHAA BIAA BJAA

Speaker: Stefan Schwartz

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Comparing selected Genotypes Tobacco BDAA

Initial growth side -2

BJAA

0 2

Secondary growth top x40

4

Initial size side

6

 BJAA shows in Top and Side view a high secondary growth  BDAA shows the exact opposite behaviour and Initial size top has a high initial growth with low secondary growth

8 10

Secondary growth side x40

Initial growth top

Speaker: Stefan Schwartz

BJAA (Lower 95%) BJAA Mean Value BJAA (Upper 95%) BDAA (Lower 95%) BDAA Mean Value BDAA (Upper 95%)

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Conclusions  excelent reproducibility of measurements (95% confidence intervals per genotype)  high sensitivity to distinguish between different genotypes already after 5 days of growth

 high correllation between „real“ phenotype and digital phenotype  It works!

Speaker: Stefan Schwartz

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Thank you for your attention!

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