Trait-Dependent Effects of Band Selection on Predicting Soybean Biomass, Leaf Area Index, and Canopy Cover from Hyperspectral Reflectance

Abstract

Predicting canopy traits non-destructively is important for understanding crop growth and improving phenotyping efficiency. Hyperspectral reflectance provides detailed spectral information, but the role of band selection in regression-based trait prediction at the canopy scale remains unclear. In this study, we evaluated the effects of different band-selection algorithms on the prediction accuracy of aboveground biomass (AGB), leaf area index (LAI), and canopy cover (CC) in soybeans across multiple sites, years, cultivars, and irrigation treatments. We compared a full-band partial least squares regression (PLS) model with three band-selection methods (PLS-Variable Importance in Projection (VIP), Bootstrapped least absolute shrinkage and selection operator (LASSO) (BoLASSO), and an ensemble approach), and model performance was assessed using independent validation datasets. The results showed that the effectiveness of band selection depended on the target trait. Full-band PLS provided the highest accuracy for AGB, whereas BoLASSO achieved comparable accuracy to PLS for LAI and CC using a reduced number of selected bands. The selected wavelengths were located mainly in the visible, red-edge, and near-infrared regions. These results indicate that band-selection strategies should be tailored to the target trait and provide a basis for efficient band design in crop phenotyping.

Plant phenotyping relevance

ハイパースペクトル反射を用いた作物形質推定について、バンド選択アルゴリズムと回帰モデルを比較・独立検証しており、フェノタイピング手法の技術評価が中心である。

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