A double-sigmoid approach for high-throughput phenotyping of winter wheat growth dynamics

Abstract

Multi-temporal data from unoccupied aerial systems (UAS) offer insights into growth parameters for winter wheat breeding decisions. Weekly UAS data were collected during the 2019 and 2020 growing seasons from dryland and irrigated nurseries at Bushland, Texas, within the Texas A&M Uniform Variety Trials. Canopy cover (CC) was extracted from orthomosaic images and modeled using a double-sigmoid function with a second-order derivative that captured genotypic variation in canopy growth and senescence with high coefficients of determination (R² > 0.99) and low root mean square error (RMSE) values ranging from 1.73 to 4.06. Analysis of variance (ANOVA) revealed highly significant genotypic effects (p<0.001) for yield, heading, and Green Leaf Area Duration (LAD) in all environments except 2020 dryland, where no significant differences among genotypes were detected. Extracted parameters showed positive correlations with agronomic traits, particularly under rainfed and stress-prone conditions. The end decrease stage (EDS) was correlated with grain yield (r=0.56 in 2019 dryland, and r=0.53 in 2020 irrigated, p<0.001), and the start decrease stage (SDS) was highly correlated with yield (r=0.53 in 2020 irrigated, p<0.001). The maximum decrease rate date (MDRD) was positively correlated with yield in 2019 dryland (r=0.55, p<0.001), while LAD had a correlation of r=0.56 in 2019 dryland and r=0.58 in 2020 irrigated (p<0.001). These findings demonstrate that double-sigmoid model provides a powerful, non-invasive framework for quantifying canopy development, senescence timing, and stress responses. By distinguishing genetics from environmental influences on canopy dynamics, this approach enhances selection accuracy and accelerates the development of stress-resilient winter wheat cultivars.

Plant phenotyping relevance

UAS画像からキャノピー被覆を抽出し、二重シグモイドモデルで生育・老化動態を定量化する手法が研究の中心であり、精度評価と遺伝型・農業形質との検証も行っている。

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