A spectral-physiological feature fusion model for the early detection of anthracnose in citrus leaves

Abstract

Citrus anthracnose is a destructive fungal disease caused by Colletotrichum gloeosporioides, which causes leaf damage, fruit rot, and yield loss in citrus production. This study proposes an early detection method for citrus leaf anthracnose that integrates spectral and physiological data. Artificial inoculation experiments showed that the infected leaves exhibited yellowish-brown lesions, and the reflectance derived from visible-near-infrared (VNIR) spectroscopy and Fourier transform near-infrared (FTNIR) spectroscopy significantly decreased. Stomatal conductance and photosynthetic rate declined 4 days after inoculation. Physiological damage to leaves caused by fungal infection was more severe than mechanical damage. Three wavelength extraction algorithms [particle swarm optimization (PSO), bootstrapping soft shrinkage (BOSS), and least absolute shrinkage and selection operator (LASSO)] were combined with three machine learning models [artificial neural network (ANN), k-nearest neighbor (KNN), and categorical boosting (CatBoost)] to perform feature-level fusion on spectral data, photosynthetic parameters, and vegetation indices to improve classification accuracy. The fusion model had high classification accuracy (0.958–0.989) and Matthews correlation coefficient (MCC) (0.917–0.978). The model achieved the best performance in distinguishing leaves with early disease symptoms from healthy leaves, with an accuracy of 0.989, an F1 score of 0.989, and an MCC of 0.978. This research provides a reliable theoretical basis and technical support for the precise identification and early prevention and control of citrus anthracnose.

Plant phenotyping relevance

柑橘葉の病害状態をスペクトル・生理計測から推定する早期検出法を開発し、特徴抽出と機械学習モデルの性能を評価しており、植物表現型取得・推定が中心である。

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