High-throughput field phenotyping of soybean: Spotting an ideotype

Abstract

Soybean is among the most important crops for food and feed production worldwide. Sustainable and local production in regions with marginal climates requires cold-adapted varieties that create high yield and protein content in a short vegetation period. Drone-based high-throughput field phenotyping methods allow monitoring the success and the developmental speed of genotypes in such target environments. This study exemplifies that such frequent and precise analyses of remotely sensed canopy growth traits can be used to derive the optimal genotype, a so-called ideotype, for a given mega-environment. For the case example of Switzerland, a country with a temperate oceanic climate, the results indicate that image-derived traits allow predicting yield and protein content from the dynamics of vegetative growth. Genotypes with early canopy cover produce high yield, whereas genotypes that show a prolonged duration until they have reached their final maximum of leaf area index are characterized by a high protein content. Analyses of early performance trial stage material indicate that there are genotypes that combine both features of growth dynamics. Whether these genotypes are then indeed successful in breeding programs remains to be investigated, since this also depends on disease resistance and other traits of those genotypes. Yet, overall, this study provides strong indications of the high value of high-throughput field phenotyping in the context of physiological and breeding-related analyses of crops.

Plant phenotyping relevance

ドローンによる高スループット圃場フェノタイピングと画像由来キャノピー形質の反復・精密推定が研究の中心であり、遺伝子型評価や収量・タンパク質予測に応用している。

Code and data availability

The supplied blocks describe drone-based soybean phenotyping (PhenoFly UAS, plant height/canopy cover/LAI extraction) but contain no public deposit of the paper's phenotype data, imagery, or analysis code. The only supplementary-data mention points to the article's own DOI, and all other URLs are cited prior work or R/

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