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High throughput assessment of plant canopy in progress

  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
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