An integrated UAV-based spectral-index fusion framework using machine learning classifiers to model physiological maturity in dry bean

Abstract

Accurate and scalable prediction of physiological maturity (PM) in leguminous crops remains a key challenge due to indeterminate growth and canopy heterogeneity. Thus, causing difficulties for optimizing breeding decisions and field management. This study develops a novel UAV-based multispectral-index fusion framework for high-throughput maturity classification in dry bean. This is conducted by combining parametric and non-parametric machine learning (ML) classification models to capture non-linear maturity signatures. Multispectral imagery was collected over three growing seasons across multiple genotypes. Six spectral bands and five maturity-relevant vegetation indices capturing chlorophyll degradation, senescence, and canopy greenness were evaluated through 63 feature-set combinations and 10 parametric and non-parametric ML classifiers. Model performance was assessed using stratified five-fold cross-validation, composite z-score aggregation, and Friedman-Nemenyi statistical ranking to jointly evaluate accuracy and consistency. Among all the model-feature combinations, the Support Vector Classifier (SVC) paired with Band_All6 + MCARI feature-set achieved the highest test accuracy (>70%), with class-wise recall of 63.2%, 71.4% and 79.4% for early, medium and late maturity, respectively. Hybrid feature-sets integrating chlorophyll-sensitive indices with spectral bands consistently outperformed index-only or band-only sets, confirming the synergistic effect of bands and engineered index coupling. This research establishes a statistically validated pipeline linking UAV multispectral data, vegetation spectral-index and machine learning classification to quantify PM variability in dry bean with potential for validation in other legume crops. The proposed framework offers a generalizable approach for non-destructive maturity prediction, enabling breeders to accelerate genotype selection and harvest scheduling under field-scale conditions.

Plant phenotyping relevance

UAVマルチスペクトル画像と機械学習による乾燥豆の生理的成熟度推定パイプラインを開発・統計検証しており、植物状態の取得・抽出手法が研究の中心である。

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