Deep learning identifies optimal single spectral band for non-destructive fresh weight estimation in cabbage: A novel paradigm for postharvest management technology

Abstract

Hyperspectral imaging (HSI) systems offer rich spectral information for precision agriculture applications such as yield forecasting, but their high cost and complexity limit widespread adoption. The relationship spectral data complexity and prediction accuracy in non-destructive crop monitoring remains unclear, challenging the assumption that more complex spectral data inherently yields better predictions. We developed a workflow integrating UAV-based HSI (150 bands) with deep learning to non-destructively estimate fresh weight of cabbage heads (n = 680). A two-dimensional convolutional neural network (2D-CNN) was deployed to identify the single most predictive wavelength through systematic feature extraction. Performance was benchmarked against 20 conventional multi-band vegetation indices (VIs). The CNN identified a single spectral band at 565.63 nm as the optimal predictor. A predictive model using only this single-band input achieved a coefficient of determination (R²) of 0.49 on an independent test set, with a root mean square error (RMSE) of 0.99 kg and a mean absolute error (MAE) of 0.84 kg. This performance substantially surpassed the best-performing conventional multi-band VI (MCARI2, R² = 0.34), representing a 44% improvement in explained variance using single-band data input. This AI-driven approach autonomously distill HSI complexity into a single optimal wavelength that outperforms established multi-band indices. The findings provide a methodological framework for designing simplified, cost-effective spectral sensors for precision agriculture, potentially improving accessibility and scalability of crop monitoring technologies.

Plant phenotyping relevance

キャベツ結球の生体重という植物形質を、UAVハイパースペクトル画像とCNNで非破壊推定するワークフローを開発し、独立テストと既存指標との比較で検証しており、形質取得手法が中心である。

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