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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1461855
This article is part of the Research Topic Plant Pest and Disease Model Forecasting: Enhancing Precise and Data-Driven Agricultural Practices View all 5 articles

Early Detection and Lesion Visualization of Pear Leaf Anthracnose Based on Multi-source Feature Fusion of Hyperspectral Imaging

Provisionally accepted
Yingying Zhang Yingying Zhang 1,2Yannan Qi Yannan Qi 2,3Meiqing Wang Meiqing Wang 4Tao Xu Tao Xu 2,3Kai Huang Kai Huang 2,3Yuanhao Sun Yuanhao Sun 2,3Quanchun Yuan Quanchun Yuan 2,3Xiaohui Lei Xiaohui Lei 2,3Xue Li Xue Li 2,3*Xiaolan Lv Xiaolan Lv 1,2,3*
  • 1 Jiangsu University, Zhenjiang, China
  • 2 Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, China
  • 3 Key Laboratory of Horticultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing, China
  • 4 Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, Zürich 8092, Switzerland, Zürich, Switzerland

The final, formatted version of the article will be published soon.

    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. The study will provide scientific theoretical support for the early prevention and precise treatment of pear leaf anthracnose disease.

    Keywords: hyperspectral imaging, Pear leaves, Anthracnose, Multi-source features, Classification model, visualization

    Received: 09 Jul 2024; Accepted: 23 Sep 2024.

    Copyright: © 2024 Zhang, Qi, Wang, Xu, Huang, Sun, Yuan, Lei, Li and Lv. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Xue Li, Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, China
    Xiaolan Lv, Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.