This document discusses the development of a high-throughput phenotyping platform at ICRISAT to evaluate important plant traits and "building blocks" related to yield under drought conditions. It describes moving from 2D to 3D imaging, testing technologies on various crops, designing the platform, and challenges with data analysis. The goal is to rapidly measure key traits like leaf area, water use, and responses to stress to advance breeding programs and understand crop adaptation in target environments. Continuous technology validation, multidisciplinary collaboration, and applying findings to crop models and breeding are emphasized.

1. From 2-D imaging and plant-to-camera-cabinet
to 3-D scanning and scanner-to-plant concept
High throughput assessment of plant
canopy in progress
Vincent Vadez – Jana Kholová
ICRISAT
Phenodays 2014 – 29-31th October 2014

2. Overview
Research concepts – relevant phenotyping
Technology testing
Designing the HT platform
• Logistics
• Installation & troubleshooting
• Bioinformatics
Future projections

3. Grain Yield
Grain Number Grain Size & N
 Biomass RADN
TE T RUE Rint
vpd
kl LAISLNRoots k

TN LNo
A >A
APSIM Generic Crop Template, from Graeme Hammer
Focus on the
“building blocks”
Which ”building blocks” are linked to yield
improvement in target agro-ecology
(SAT – drought)?
Yield is not a trait
(GxExM)
Research concepts – relevant phenotyping

4. Lysimetric facility at ICRISAT
• Field-like
• Gravimetric - manual (WU, TE)
• Long term (3 Wks-maturity)
• Medium throughput (5000 PVCs/wk)
• Stress scenarios
Relevant yield building blocks?

5. 0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0 1 2 3 4
WU(kgplant-1week-1)
Weeks after panicle emergence
ICMH01029
ICMH01040
ICMH01046
PRLT2/89-33
Vadez et al 2013 – Plant Soil
H77/833-2
ICMH02042
Terminal drought
sensitive
Terminal drought
adapted
Crop adaptation to post-rainy season cultivation:
less WU at vegetative stage =
more water left for reproduction & grain filling
Water extraction pattern (WS) in pearl millet
Flowering
stress
Relevant yield building blocks?

6. R² = 0.7108
0
4
8
12
16
20
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
WU in week 3 after panicle emergence
GrainYield(gplant-1)
Relationship between grain yield and water use
Relevant yield building blocks?
Pre-anthesis WU defines success of
grain-filling in post-rainy cultivation systems

7. Relevant “building blocks”
for rain-fed agricultureLA
Thermal time Vapor Pressure Deficit (VPD; kPa)
Transpirationrate(gcm-2h-1)
0.0 2.0 4.0
0.0
1.0
WU dynamics =
LA x LA conductivity

8. • Outdoors – environment of growth matters
• Rapid access to water use at key time
• Early development defines crop success
• Rapid evaluation of environmental effects
(Soil moisture, VPD)
What platform for phenotyping?
LA scanner Leaf area development
scales
Water extraction dynamics
WU dynamics =LA x LA conductivity
[transpiration/LA]

9. Plant Eye prototypeTechnology testing
Technology limits in outdoor environment
• Light, wind, plant structure
Development of hardware/software
• Reciprocal learning process

10. Legumes – cowpea, chickpea, groundnut, pigeonpea, lentil
Cereals – maize, sorghum, millet
Technology testing - validation
peanut
y = 42.162x + 2940.1
R² = 0.9381
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
0 100 200 300 400 500 600 700 800 900 1000
3D-LA(mm2)
destructive LA (cm2)
1000 cm2
R² = 0.9273
y = 55.466x + 7842.7
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
0 200 400 600 800 1000 1200 1400
3D-LA(mm2)
destructive LA (cm2)
cowpea
1200 cm2
y = 52.994x + 3506.4
R² = 0.7045
0
5000
10000
15000
20000
25000
30000
35000
0 100 200 300 400 500
3D-LA(mm2)
destructive LA (cm2)
chickpea
400 cm2
y = 21.665x + 24155
R² = 0.8635
0
20000
40000
60000
80000
100000
120000
0 1000 2000 3000 4000
3D-LA(mm2)
destructive LA (cm2)
millet
3000 cm2

11. y = 42.162x + 2940.1
y = 55.466x + 7842.7
y = 52.994x + 3506.4
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
0 500 1000 1500
3D-LA(mm2)
destructive LA (cm2)
peanut
cowpea
chickpea
LA-scan=
Size & structure
y = 25.955x + 4254.7
R² = 0.8525
y = 34.002x + 3224.5
R² = 0.9206
0
20000
40000
60000
80000
100000
120000
140000
160000
0 1000 2000 3000 4000 5000
3DLA(mm2)
destructive LA (cm2)
sorghum
pearl millet
Technology testing - validation
Legumes
LA- scan=
more accurate
surface of exchange?
Cereals

12. 1450
6256251750
350
15000
400600
2Position2Position
Rail
Wheel track
1000
12
5000
3500
Installation
Specifications:
No. of sectors: 3200 - 4800
SSD Scan Velocity: 50 mm/s
SSD Velocity: max. 250 mm/s
PlantEye Systems: 8
Throughput: 2000 - 3000 sectors/h
Platform length: 129,5 m
Load cells Scanners
Meteo-station
Irrigation

13. Bioinformatics
Massive time series
Computational exercise
Selection of data???
Filtering the data??
Which data
express canopy
properties
the best???

14. Selection of relevant data expressing canopy properties
- What data are the most representative of crops canopy
size & structure in outdoors conditions - legumes?
y = 26.23x + 13346
R² = 0.8198
0
20000
40000
60000
80000
100000
120000
0 1000 2000 3000 4000
3Dleafarea(mm2)
Destructive LA (cm2)
LA extracted 4 h in the morning
peanut
y = 24.832x + 9606.3
R² = 0.9126
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000
0 1000 2000 3000 4000
3Dleafarea(mm2)
Destructive LA (cm2)
LA extracted for whole day
Open canopy
during the day
Closed canopy
during the night
Which scans express whole canopy the best?

15. y = 34.002x + 3224.5
R² = 0.9206
0
20000
40000
60000
80000
100000
120000
140000
160000
0 1000 2000 3000 4000 5000
3DLA(mm2)
Destructive LA (cm2)
LA extracted at night
863B
H77/833-2
PRLT
Selection of relevant data expressing canopy properties
- What data are the most representative of crops canopy
size & structure in outdoors conditions – cereals?
y = 30.876x + 6258.7
R² = 0.859
0
20000
40000
60000
80000
100000
120000
140000
160000
0 1000 2000 3000 4000 5000
3DLA(mm2)
Destructive LA (cm2)
LA extracted during morning
863B
H77/833-2
PRLT
Canopy
&
wind
Canopy
&
no wind
Which scans express whole canopy the best?

16. y = 34.002x + 3224.5
R² = 0.9206
0
20000
40000
60000
80000
100000
120000
140000
160000
0 1000 2000 3000 4000 5000
3DLA(mm2)
Destructive LA (cm2)
863B
H77/833-2
PRLT
Different genotypes may not fit the same regression
ChickpeaPearl millet
R² = 0.6652
R² = 0.8095
R² = 0.7994
R² = 0.7405
R² = 0.7236
R² = 0.5719
0
20000
40000
60000
80000
100000
120000
0 500 1000 1500 2000 25003DLA(mm2)
Destructive LA (cm2)
ICC3325
ICC4567
ICC4958
ICCV92944
ICC1205
ICC5912
Selection of relevant data expressing canopy properties
- Are all genotypes’ LA within species related to LA scan similarly??

17. • Rapid evaluation of environmental effects
Application of NaCl to pearl millet
NaCl
application
Re-watering
Change in rate of
water extraction
night
day
NaCl
H2O

18. -200
-100
0
100
200
300
400
500
grain/stoveryieldbenefits/loss
(kgha-1)
tover
grain
Value of building blocks???
0
500
1000
1500
2000
2500
200 400 600 800
LA(cm2)
thermal time (degree days)
Canopy
growth
t
Soilwater
0
Water
extraction
S/D
leafgrowthrate
10
LA growth
in WS
Variability in building blocks translates
into the crop model parameters
Day course
(h)
Tr
0
VPD
Limited
TR
Yield
improvement/loss
Field
testing???

19. 0
500
1000
1500
2000
2500
200 300 400 500 600 700 800
LA(cm2)
thermal time (degree days)
S35
7001
6008
6026
6040
6016
Canopy growth Future projections
Variability in yield
“building blocks”
 Value of variability in
building blocks ($ ha-1)
 Acceleration of breeding
 Breeding specific for
target agro-ecologies
Yield improvement

20. Take-home message:
Phenotyping for the relevant building blocks
(assessing causes of drought adaptation rather than consequences!)
Phenotyping outdoors/enviromnental variables
(environment of development matters!)
Validation of technology & tech. development
(each crop/conditions are different – link to developers)
Bioinformatics
(learning of the best ways of working with massive datasets)
Phenotyping is a continuous learning process
HT-phenotyping - a way to precision agriculture!

21. Development requires multidisciplinarity!
• Technology developers
• Bioinformatists
• Physiologists
• Breeders
• Modelers
Thank you
Mission
To reduce poverty, hunger,
malnutrition and environmental
degradation in the dryland tropics