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LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget.

Vadez V, Kholová J, Hummel G, Zhokhavets U, Gupta SK, Hash CT - J. Exp. Bot. (2015)

Bottom Line: Close agreement between scanned and observed leaf area data of individual plants in different crops was found (R(2) between 0.86 and 0.94).Similar agreement was found when comparing scanned and observed area of plants cultivated at densities reflecting field conditions (R(2) between 0.80 and 0.96).This new platform has the potential to phenotype for traits controlling plant water use at a high rate and precision, of critical importance for drought adaptation, and creates an opportunity to harness their genetics for the breeding of improved varieties.

View Article: PubMed Central - PubMed

Affiliation: ICRISAT-Crop Physiology Laboratory, Greater Hyderabad, Patancheru 502324, Telangana, India v.vadez@cgiar.org.

No MeSH data available.


Validation experiments using the PlantEyeR technology prototype: leaf area of individual plants of (A) peanut, (B) cowpea and (C) pearl millet, in which leaf area was assessed destructively (observed leaf area) (Li3000, LICOR, Lincoln, Nebraska, USA) and compared to 3D-leaf area generated by the scanners, during different phases of plant development. For each plant species, two genotypes differing with the canopy structure were used (open and closed circles). In this experiment with a prototype scanner, the scanning width was only 32cm, compared to 65cm in the current plaftform (Fig. 5), which restricted resolution for pearl millet.
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Figure 4: Validation experiments using the PlantEyeR technology prototype: leaf area of individual plants of (A) peanut, (B) cowpea and (C) pearl millet, in which leaf area was assessed destructively (observed leaf area) (Li3000, LICOR, Lincoln, Nebraska, USA) and compared to 3D-leaf area generated by the scanners, during different phases of plant development. For each plant species, two genotypes differing with the canopy structure were used (open and closed circles). In this experiment with a prototype scanner, the scanning width was only 32cm, compared to 65cm in the current plaftform (Fig. 5), which restricted resolution for pearl millet.

Mentions: A prototype scanner was initially installed and tested to assess individual plants with a scanning width of 32cm, a length of 33cm and a maximum scanning height of 80cm. Although these parameters were restrictive, they were sufficient for testing, and were enhanced in upgraded versions of the scanner. The scanning width in the current setup is now 65cm, the length is either 40 or 60cm and the scanning height is 100cm. Validation was carried out with three species (peanut, cowpea and pearl millet) to reflect a wide range of canopy architecture, and two genotypes with putatively different leaf areas in each species. Plants were grown individually in 27cm diameter pots containing approximately 11kg of soil and held under optimal growth conditions. At regular intervals, until 6–7 weeks after sowing (depending on the crop), sets of plants were scanned, destructively harvested and leaf area was measured with a LI3000 leaf area meter (LICOR, Lincoln, Nebraska, USA). Overall, there was a very good fit between the observed leaf area data and the leaf area data derived from the scanner analysis of the 3D images (i.e. the 3D-LA) (Fig. 4, initial validation). The regression coefficient varied from 0.86 (pearl millet, Fig. 4A) to 0.93 (cowpea or peanut, Fig. 4B, C). It is notable that the scanners were able to reasonably estimate the leaf area of very large plants (leaf areas up to 3000cm2 in individual pearl millet plants). Even before reaching that size, the leaves were going beyond the sector perimeters, leading to some error between observed leaf area and 3D-LA, and explaining the lower R-square in the case of pearl millet. Since the data was gathered only by one perspective, the option exists for more accurate measurement by adding another scanner with a different perspective, the choice of this being a trade-off between the added precision on the phenotype versus the added cost (including computation and storage). The scanners are currently optimised and the fitness is similar to those presented here (Kholová et al., unpublished).


LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget.

Vadez V, Kholová J, Hummel G, Zhokhavets U, Gupta SK, Hash CT - J. Exp. Bot. (2015)

Validation experiments using the PlantEyeR technology prototype: leaf area of individual plants of (A) peanut, (B) cowpea and (C) pearl millet, in which leaf area was assessed destructively (observed leaf area) (Li3000, LICOR, Lincoln, Nebraska, USA) and compared to 3D-leaf area generated by the scanners, during different phases of plant development. For each plant species, two genotypes differing with the canopy structure were used (open and closed circles). In this experiment with a prototype scanner, the scanning width was only 32cm, compared to 65cm in the current plaftform (Fig. 5), which restricted resolution for pearl millet.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4585418&req=5

Figure 4: Validation experiments using the PlantEyeR technology prototype: leaf area of individual plants of (A) peanut, (B) cowpea and (C) pearl millet, in which leaf area was assessed destructively (observed leaf area) (Li3000, LICOR, Lincoln, Nebraska, USA) and compared to 3D-leaf area generated by the scanners, during different phases of plant development. For each plant species, two genotypes differing with the canopy structure were used (open and closed circles). In this experiment with a prototype scanner, the scanning width was only 32cm, compared to 65cm in the current plaftform (Fig. 5), which restricted resolution for pearl millet.
Mentions: A prototype scanner was initially installed and tested to assess individual plants with a scanning width of 32cm, a length of 33cm and a maximum scanning height of 80cm. Although these parameters were restrictive, they were sufficient for testing, and were enhanced in upgraded versions of the scanner. The scanning width in the current setup is now 65cm, the length is either 40 or 60cm and the scanning height is 100cm. Validation was carried out with three species (peanut, cowpea and pearl millet) to reflect a wide range of canopy architecture, and two genotypes with putatively different leaf areas in each species. Plants were grown individually in 27cm diameter pots containing approximately 11kg of soil and held under optimal growth conditions. At regular intervals, until 6–7 weeks after sowing (depending on the crop), sets of plants were scanned, destructively harvested and leaf area was measured with a LI3000 leaf area meter (LICOR, Lincoln, Nebraska, USA). Overall, there was a very good fit between the observed leaf area data and the leaf area data derived from the scanner analysis of the 3D images (i.e. the 3D-LA) (Fig. 4, initial validation). The regression coefficient varied from 0.86 (pearl millet, Fig. 4A) to 0.93 (cowpea or peanut, Fig. 4B, C). It is notable that the scanners were able to reasonably estimate the leaf area of very large plants (leaf areas up to 3000cm2 in individual pearl millet plants). Even before reaching that size, the leaves were going beyond the sector perimeters, leading to some error between observed leaf area and 3D-LA, and explaining the lower R-square in the case of pearl millet. Since the data was gathered only by one perspective, the option exists for more accurate measurement by adding another scanner with a different perspective, the choice of this being a trade-off between the added precision on the phenotype versus the added cost (including computation and storage). The scanners are currently optimised and the fitness is similar to those presented here (Kholová et al., unpublished).

Bottom Line: Close agreement between scanned and observed leaf area data of individual plants in different crops was found (R(2) between 0.86 and 0.94).Similar agreement was found when comparing scanned and observed area of plants cultivated at densities reflecting field conditions (R(2) between 0.80 and 0.96).This new platform has the potential to phenotype for traits controlling plant water use at a high rate and precision, of critical importance for drought adaptation, and creates an opportunity to harness their genetics for the breeding of improved varieties.

View Article: PubMed Central - PubMed

Affiliation: ICRISAT-Crop Physiology Laboratory, Greater Hyderabad, Patancheru 502324, Telangana, India v.vadez@cgiar.org.

No MeSH data available.