Non-Destructive Yield Prediction in Common Bean Using UAV-Based Spectral and Structural Metrics: Implications for Sustainable Crop Management

Abstract

Early prediction of common bean (Phaseolus vulgaris L.) yield is essential for improving productivity in tropical agricultural systems. In this study, we integrated canopy structural metrics obtained with the Tracing Radiation and Architecture of Canopies (TRAC) system, unmanned aerial vehicle (UAV)-based multispectral measurements (normalized difference vegetation index—NDVI, projected canopy area), and phenological variables collected from stages R6 to R8 under non-limiting nitrogen conditions. Exploratory analyses (correlation, variance inflation factors—VIF), dimensionality reduction (principal component analysis—PCA), and regularized regression (Elastic Net/LASSO), combined with bootstrap stability selection, were applied to identify a parsimonious subset of robust predictors. The final model, composed of six variables, explained approximately 72% of the variability in plant-level grain yield, with acceptable errors (RMSE ≈ 10.67 g; MAE ≈ 7.91 g). The results demonstrate that combining early vigor, radiation interception, and canopy architecture provides complementary information beyond simple spectral indices. This non-destructive framework delivers an efficient model for early yield estimation and supports site-specific management decisions in common bean with high spatial resolution. By enhancing input-use efficiency and reducing waste, this approach contributes to sustainable development and aligns with the global Sustainable Development Goals (SDGs) for climate-resilient agriculture.

Plant phenotyping relevance

UAVマルチスペクトル計測、キャノピー構造指標、特徴選択・回帰モデルを統合し、植物レベルの収量を非破壊推定する方法が研究の中心である。

Code and data availability

The paper's plant-level phenotyping dataset (TRAC structural measurements, UAV-derived NDVI/canopy metrics, and per-plant grain yield) is not publicly deposited; the authors state it is available only upon request. The public supplement (Table S1) is only a data dictionary/metadata, not the measurements or analysis. No

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