1. QTL for vigor traits (LA, plant height, growth rate)
LeasyScan: A novel concept combining 3D imaging and lysimetry for HTP of traits controlling plant water budget
ABSTRACT:Relevant phenotyping is a main bottleneck and new technologies provide opportunities for easier, faster, more sensitive and more informative phenotyping. Studies on drought adaptation showed that water availability during the grain filling period was critical for increasing yield and depended on traits affecting the plant water budget at earlier stages. LeasyScan was developed to assess canopy traits affecting water use (leaf area, leaf area index, transpiration), based on a novel 3D scanning technique, a scanner-to-plant concept to increase imaging throughput, and analytical scales for transpiration measurements. Close agreement was found in different crops between scanned and observed leaf area data of individual plants (R2 between 0.86 and 0.94) or of plants cultivated at field densities (R2 between 0.80 and 0.96). This new platform opens the opportunity to harness their genetics of traits controlling plant water use, of critical importance for drought adaptation, towards the breeding of improved varieties.
RESULTS
INTRODUCTION - New sensor technologies are now available for phenotyping. But the use of that technology has to be driven by scientific questions. All starts with the proper framing of a target phenotype. Studies on drought adaptation has shown that water availability during the grain filling period was critical. A HTP platform, LeasyScan was then developed to target traits controlling plant water use and included:
3D scanning to follow leaf area development
Analytical scales to continuously monitor plant water use A B C
3D leaf area development in pearl millet fine mapping recombinants varying at three marker loci within the terminal drought tolerance QTL region of LG2 (left), and in pearl millet hybrids bred for different rainfall zones of India: A1, < mm; A, B > 400mm (right)
Observed leaf area of plants grown under field-like density (A-peanut, 24 plant m-2, B-cowpea & C-pearl millet, 16 plant m-2) compared to scanned area (3D-Leaf area)
MATERIALS AND METHODS
Canopy conductance profile as a function of thermal time in two sorghum genotypes (VPD-insensitive R16 and VPD-sensitive S35). Differences between the open and close symbol curves represent water savings at high vapor pressure deficit (VPD).
Load Cells
PlantEye F300
Frequency: 50 XZ profile / s
Temperature range: 0…40 °C
Humidity: < 90% rel.
Power: V
Laser class: 1M
Measures: 485 x 240 x 110
Weight: 3,4 kg
Environmental Protection Rating IP 67
Data transfer: WiFi
Right: Distances used in the computation. TH is the target height used as a reference
Left: 3D object is reconstructed from 2D images (50-80 images s-1) of the reflection (red) of laser line (green) projected from the scanner (PlantEyeR) on the canopy.
Mapping of traits related to plant vigor (Leaf area, plant height, growth rate, et...) on LG4 of chickpea (right), co-mapping with earlier identified root QTL (left)
Information in time on plant (top) or environment (bottom) parameters visualized through web-based interface (HortcontrolR) for quality control
Eight scanners (PlantEyeR) can assess /4800 sectors (60×60/60×40) in 2h intervals with standard speed of 50 mm s-1
LeasyScan follows up plant height and canopy size (3D area, projected leaf area).
Load cells allows a continuous assessment of plant water use.
QTL for vigor traits (LA, plant height, growth rate)
Sensors:
Temperature, RH%, rainfall, solar radiation, wind speed
DISCUSSIONS–
Platform is up and running - Upgrade with 1488 load cells completed in June 2016
New algorithm being developed (toward canopy architecture)
Next steps: (i) add other sensors to the scanners (VIS-RGB) (ii) develop field-based phenotyping using LeasyScan for validation (early NDVI indices, thermal sensing)
3D-point clouds accessed from Hortcontrol
Lead Author: V Vadez1, J Kholová1, G Hummel2, U Zhokhavets2, SK Gupta1, C Tom Hash3
1ICRISAT-India - 2 Phenospex Ltd - 3 ICRISAT-Niger
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