Hyperspectral Sensing of Plant Diseases: Principle and Methods

文献类型： 外文期刊

第一作者： Wan, Long

作者： Wan, Long;Li, Hui;Li, Chengsong;Wang, Pei;Wang, Aichen;Wang, Pei;Yang, Yuheng;Wang, Pei;Wang, Pei

作者机构：

关键词： hyperspectral imaging technology; plant disease identification; photo response; machine learning

期刊名称：AGRONOMY-BASEL （ 影响因子：3.949； 五年影响因子：4.117 ）

ISSN：

年卷期： 2022 年 12 卷 6 期

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收录情况： SCI

摘要： Pathogen infection has greatly reduced crop production. As the symptoms of diseases usually appear when the plants are infected severely, rapid identification approaches are required to monitor plant diseases at early the infection stage and optimize control strategies. Hyperspectral imaging, as a fast and nondestructive sensing technology, has achieved remarkable results in plant disease identification. Various models have been developed for disease identification in different plants such as arable crops, vegetables, fruit trees, etc. In these models, important algorithms, such as the vegetation index and machine learning classification and methods have played significant roles in the detection and early warning of disease. In this paper, the principle of hyperspectral imaging technology and common spectral characteristics of plant disease symptoms are discussed. We reviewed the impact mechanism of pathogen infection on the photo response and spectrum features of the plants, the data processing tools and algorithms of the hyperspectral information of pathogen-infected plants, and the application prospect of hyperspectral imaging technology for the identification of plant diseases.

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