Unverified paper record
Spectral Reflectance Indices as a High Throughput Selection Tool in a Sesame Breeding Scheme
Remote Sensing · 31 May 2022 · 10.3390/rs14112629
Abstract
On-farm genotype screening is at the core of every breeding scheme, but it comes with a high cost and often high degree of uncertainty. Phenomics is a new approach by plant breeders, who use optical sensors for accurate germplasm phenotyping, selection and enhancement of the genetic gain. The objectives of this study were to: (1) develop a high-throughput phenotyping workflow to estimate the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge index (NDRE) at the plot-level through an active crop canopy sensor; (2) test the ability of spectral reflectance indices (SRIs) to distinguish between sesame genotypes throughout the crop growth period; and (3) identify specific stages in the sesame growth cycle that contribute to phenotyping accuracy and functionality and evaluate the efficiency of SRIs as a selection tool. A diversity panel of 24 sesame genotypes was grown at normal and late planting dates in 2020 and 2021. To determine the SRIs the Crop Circle ACS-430 active crop canopy sensor was used from the beginning of the sesame reproductive stage to the end of the ripening stage. NDVI and NDRE reached about the same high accuracy in genotype phenotyping, even under dense biomass conditions where “saturation” problems were expected. NDVI produced higher broad-sense heritability (max 0.928) and NDRE higher phenotypic and genotypic correlation with the yield (max 0.593 and 0.748, respectively). NDRE had the highest relative efficiency (61%) as an indirect selection index to yield direct selection. Both SRIs had optimal results when the monitoring took place at the end of the reproductive stage and the beginning of the ripening stage. Thus, an active canopy sensor as this study demonstrated can assist breeders to differentiate and classify sesame genotypes.
Plant phenotyping relevance
ゴマ遺伝子型の表現型を推定するための作物群落センサーによる高スループット測定ワークフローを開発・評価しており、センサー取得と選抜性能の検証が中心である。
abstractdevelop a high-throughput phenotyping workflow to estimate the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge index (NDRE) at the plot-level through an active crop canopy sensor
abstractevaluate the efficiency of SRIs as a selection tool
abstractThus, an active canopy sensor as this study demonstrated can assist breeders to differentiate and classify sesame genotypes.
Code and data availability
The supplied blocks describe sesame NDVI/NDRE phenotyping with a Crop Circle ACS-430 sensor and R-based analysis (Metan, Agricolae), but contain no public phenotype dataset, sensor data deposit, author analysis code, or supplement with an explicit availability statement or authors' URL. The only URLs are generic (R-FAO
No evidence-backed public reproduction asset is currently recorded.
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