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Early detection and lesion visualization of pear leaf anthracnose based on multi-source feature fusion of hyperspectral imaging

文献类型： 外文期刊

作者： Zhang, Yingying 1 ; Li, Xue 2 ; Wang, Meiqing 4 ; Xu, Tao 2 ; Huang, Kai 2 ; Sun, Yuanhao 2 ; Yuan, Quanchun 2 ; Lei, Xiaohui 2 ; Qi, Yannan 2 ; Lv, Xiaolan 1 ;

作者机构： 1.Jiangsu Univ, Sch Agr Engn, Zhenjiang, Peoples R China

2.Jiangsu Acad Agr Sci, Inst Agr Facil & Equipment, Nanjing, Jiangsu, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Hort Equipment, Nanjing, Peoples R China

4.Eidgenoss TH ETH Zurich, Inst Agr Sci, Dept Environm Syst Sci, Zurich, Switzerland

关键词： hyperspectral imaging; pear leaves; anthracnose; multi-source features; classification model; visualization

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）

ISSN： 1664-462X

年卷期： 2024 年 15 卷

页码：

收录情况： SCI

摘要： Pear anthracnose, caused by Colletotrichum bacteria, is a severe infectious disease that significantly impacts the growth, development, and fruit yield of pear trees. Early detection of pear anthracnose before symptoms manifest is of great importance in preventing its spread and minimizing economic losses. This study utilized hyperspectral imaging (HSI) technology to investigate early detection of pear anthracnose through spectral features, vegetation indices (VIs), and texture features (TFs). Healthy and diseased pear leaves aged 1 to 5 days were selected as subjects for capturing hyperspectral images at various stages of health and disease. Characteristic wavelengths (OWs1 and OWs2) were extracted using the Successive Projection Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) algorithm. Significant VIs were identified using the Random Forest (RF) algorithm, while effective TFs were derived from the Gray Level Co-occurrence Matrix (GLCM). A classification model for pear leaf early anthracnose disease was constructed by integrating different features using three machine learning algorithms: Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Back Propagation Neural Network (BPNN). The results showed that: the classification identification model constructed based on the feature fusion performed better than that of single feature, with the OWs2-VIs-TFs-BPNN model achieving a highest accuracy of 98.61% in detection and identification of pear leaf early anthracnose disease. Additionally, to intuitively and effectively monitor the progression and severity of anthracnose in pear leaves, the visualization of anthracnose lesions was achieved using Successive Maximum Angle Convex Cone (SMACC) and Spectral Information Divergence (SID) techniques. According to our research results, the fusion of multi-source features based on hyperspectral imaging can be a reliable method to detect early asymptomatic infection of pear leaf anthracnose, and provide scientific theoretical support for early warning and prevention of pear leaf diseases.

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