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Conventional resistance assessment techniques, which are largely reliant on subjective manual evaluation, fail to meet the demands for precision and scalability required for advanced genetic research. This study introduces a robust evaluation framework utilizing feature selection and optimization algorithms to enhance the accuracy and efficiency of the severity assessment of cotton VW. We conducted comprehensive time-series UAV hyperspectral imaging (400 to 995 nm) on the cotton canopy in a field environment on different days after sowing (DAS). After preprocessing the hyperspectral data to extract wavelet coefficients and vegetation indices, various feature selection methods were implemented to select sensitive spectral features for cotton VW. By leveraging these selected features, we developed machine learning models to assess the severity of cotton VW at the canopy scale. Model validation revealed that the performance of the assessment models responded dynamically as VW progressed and achieved the highest R2 of 0.5807 at DAS 80, with an RMSE of 6.0887. Optimization algorithms made a marked improvement for SVM in severity assessment using all observation data, with R2 increasing from 0.6986 to 0.9007. This study demonstrates the potential of feature selection and machine learning methods based on hyperspectral data in enhancing VW management, promising advancements in high-throughput automated disease assessment, and supporting sustainable agricultural practices.<\/jats:p>","DOI":"10.3390\/rs16244637","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T06:44:05Z","timestamp":1733899445000},"page":"4637","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Severity Assessment of Cotton Canopy Verticillium Wilt by Machine Learning Based on Feature Selection and Optimization Algorithm Using UAV Hyperspectral Data"],"prefix":"10.3390","volume":"16","author":[{"given":"Weinan","family":"Li","sequence":"first","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration on Precision Agricultural Aviation Pesticide Spraying Technology, South China Agricultural University, Guangzhou 510642, China"},{"name":"Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572024, China"}]},{"given":"Yang","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration on Precision Agricultural Aviation Pesticide Spraying Technology, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Weiguang","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration on Precision Agricultural Aviation Pesticide Spraying Technology, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Longyu","family":"Huang","sequence":"additional","affiliation":[{"name":"Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572024, China"},{"name":"Cotton Research Institute, Chinese Academy of Agricultural Sciences, Anyang 455000, China"}]},{"given":"Jianhua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572024, China"},{"name":"Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"given":"Jun","family":"Peng","sequence":"additional","affiliation":[{"name":"Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572024, China"},{"name":"Cotton Research Institute, Chinese Academy of Agricultural Sciences, Anyang 455000, China"}]},{"given":"Yubin","family":"Lan","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration on Precision Agricultural Aviation Pesticide Spraying Technology, South China Agricultural University, Guangzhou 510642, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Man, M., Zhu, Y., Liu, L., Luo, L., Han, X., Qiu, L., Li, F., Ren, M., and Xing, Y. 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