Non-destructive estimation of SPAD and biomass in Lamiophlomis rotata using hyperspectral imaging and deep learning with DRSA-CARS feature selection.

Abstract

Introduction Monitoring the growth status and aboveground biomass of wild and cultivated medicinal herbs remains a persistent challenge in precision agriculture. Methods In this study, we developed machine learning and deep learning models to estimate SPAD values and biomass of Lamiophlomis rotata (Benth.). The models used hyperspectral data and time-series phenotypic traits from 508 samples collected across different altitudes. Regions of interest (ROIs) were manually defined from plant contours. The corresponding mean spectral profiles were then preprocessed. To improve feature selection, we proposed a Dynamic Reptile Search Algorithm-enhanced CARS (DRSA-CARS) method. This method integrates a dynamic behavioral strategy into the CARS framework to identify informative spectral bands. Vegetation indices (VIs) and gray-level co-occurrence matrix (GLCM)-based texture parameters were extracted and combined with spectral features to construct the PLSR, SVR, FNN, and CNN models. Results Compared to CARS, the DRSA-CARS method reduced feature dimensionality by up to 75.7% for SPAD and 29.2% for biomass, while improving prediction accuracy ( R ²) by 24.4% and 34.7%, respectively. Among all models, the FNN achieved the highest performance, with R ² values of 0.7732 (training) and 0.7502 (testing) for SPAD and 0.8260 and 0.7933 for biomass. Feature fusion further improved predictive accuracy by 11% for SPAD and 30% for biomass compared to models based on individual feature types. Discussion These results demonstrate that coupling DRSA-CARS-based feature selection with deep learning provides a robust, non-destructive approach for evaluating plant growth status. This framework highlights the potential of hyperspectral imaging as a rapid, reliable, non-invasive tool for precision cultivation of medicinal herbs.

Plant phenotyping relevance

ハイパースペクトル画像からSPAD値とバイオマスという植物形質を非破壊推定し、特徴選択法と深層学習モデルを開発・評価しており、フェノタイピング手法が中心である。

Code and data availability

本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。