Field phenotyping using multispectral imaging in pea (Pisum sativum L) and chickpea (Cicer arietinum L)

Abstract

Pea (Pisum sativum L) and chickpea (Cicer arietinum L) are important grain legumes grown in the Palouse region of the Pacific Northwest United States. The USDA-ARS grain legume breeding program in this region focuses on developing pea and chickpea varieties with high yield potential, resistance to biotic and abiotic stresses, and superior agronomic characteristics. In this study, aerial high resolution multispectral imaging was evaluated to phenotype yield potential differences among genotypes in green pea, yellow pea and chickpea. Five experiments (three field pea and two chickpea) with 10–25 varieties grown at two locations (Pullman, Washington; Genesee, Idaho) were assessed. Images were acquired approximately 60, 70 and 90 days after planting (DAP) at 110 m above ground level. Normalized difference vegetation index (NDVI), green normalized difference vegetation index, soil adjusted vegetation index (SAVI) and simple ratio (SR) image based features (SUM, MIN, MAX, MEAN) were extracted. In most cases, the MEAN NDVI data was found to be consistently correlated with dry seed yield (p < 0.05), with green pea genotypes showing strongest relationship (r = 0.64–0.93 at about 70 DAP, both during “plot-by-plot” and “by genotype” comparisons). The MEAN SAVI and SR values were also strongly correlated with yield at 61–72 DAP in most of the pea experiments. The data collected during flowering and early pod development phenological growth stages was found to be useful in yield estimation. The developed methods can be used for early generation evaluation in breeding programs, where yield cannot be estimated due to limited seed availability.

Plant phenotyping relevance

航空マルチスペクトル画像から植生指数を抽出し、遺伝子型別の収量ポテンシャルを推定する手法を開発・評価しており、表現型取得と推定が研究の中心です。

Code and data availability

The supplied blocks describe UAV multispectral image processing (ArcGIS, SAS) and correlation analyses for pea/chickpea yield prediction, but contain no data availability statement, no public phenotype/image dataset, and no author code repository or URL. No paper-specific public asset is identifiable.

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