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Fund of Key Laboratory of Oasis Eco-agriculture, XPCC","award":["2020AB005"],"award-info":[{"award-number":["2020AB005"]}]},{"name":"Open Fund of Key Laboratory of Oasis Eco-agriculture, XPCC","award":["201801"],"award-info":[{"award-number":["201801"]}]},{"name":"Open Fund of Key Laboratory of Oasis Eco-agriculture, XPCC","award":["202003"],"award-info":[{"award-number":["202003"]}]},{"name":"Open Fund of Key Laboratory of Oasis Eco-agriculture, XPCC","award":["Y2021047"],"award-info":[{"award-number":["Y2021047"]}]},{"name":"Open Fund of Key Laboratory of Oasis Eco-agriculture, XPCC","award":["XJ2022G115"],"award-info":[{"award-number":["XJ2022G115"]}]},{"name":"Changping Huang was supported by Youth Innovation Promotion Association, CAS","award":["41971321"],"award-info":[{"award-number":["41971321"]}]},{"name":"Changping Huang was supported by Youth Innovation Promotion Association, CAS","award":["ZDBS-LY-DQC012"],"award-info":[{"award-number":["ZDBS-LY-DQC012"]}]},{"name":"Changping Huang was supported by Youth Innovation Promotion Association, CAS","award":["2020AB005"],"award-info":[{"award-number":["2020AB005"]}]},{"name":"Changping Huang was supported by Youth Innovation Promotion Association, CAS","award":["201801"],"award-info":[{"award-number":["201801"]}]},{"name":"Changping Huang was supported by Youth Innovation Promotion Association, CAS","award":["202003"],"award-info":[{"award-number":["202003"]}]},{"name":"Changping Huang was supported by Youth Innovation Promotion Association, CAS","award":["Y2021047"],"award-info":[{"award-number":["Y2021047"]}]},{"name":"Changping Huang was supported by Youth Innovation Promotion Association, CAS","award":["XJ2022G115"],"award-info":[{"award-number":["XJ2022G115"]}]},{"name":"China Xinjiang Uygur Autonomous Region Graduate Scientific Research Innovation Project","award":["41971321"],"award-info":[{"award-number":["41971321"]}]},{"name":"China Xinjiang Uygur Autonomous Region Graduate Scientific Research Innovation Project","award":["ZDBS-LY-DQC012"],"award-info":[{"award-number":["ZDBS-LY-DQC012"]}]},{"name":"China Xinjiang Uygur Autonomous Region Graduate Scientific Research Innovation Project","award":["2020AB005"],"award-info":[{"award-number":["2020AB005"]}]},{"name":"China Xinjiang Uygur Autonomous Region Graduate Scientific Research Innovation Project","award":["201801"],"award-info":[{"award-number":["201801"]}]},{"name":"China Xinjiang Uygur Autonomous Region Graduate Scientific Research Innovation Project","award":["202003"],"award-info":[{"award-number":["202003"]}]},{"name":"China Xinjiang Uygur Autonomous Region Graduate Scientific Research Innovation Project","award":["Y2021047"],"award-info":[{"award-number":["Y2021047"]}]},{"name":"China Xinjiang Uygur Autonomous Region Graduate Scientific Research Innovation Project","award":["XJ2022G115"],"award-info":[{"award-number":["XJ2022G115"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Early diagnosis of cotton verticillium wilt (VW) and accurate assessment of the disease degree are important prerequisites for preventing the large-scale development of cotton VW. Hyperspectral techniques have been widely used for monitoring the extent of plant diseases, but early detection of VW disease in cotton remains a challenge. In this study, the Boruta algorithm was used to select the key physiological characteristics (leaf temperature, chlorophyll a content, and equivalent water thickness) of cotton leaves at the early stage of VW disease, and then the Relief-F algorithm was used to select the spectral features indicating multiple \u201csymptoms\u201d of cotton VW disease at the early stage. In addition, a new cotton VW early monitoring indicator (CVWEI) was constructed by combining the weights of the new index and related bands using a hierarchical analysis (AHP) and entropy weighting method (EWM). The study showed that the physiological indices constructed under VW stress were better indicators of VW disease than traditional vegetation indices; CVEWI achieved a high accuracy of 95% in the test set, with a Kappa coefficient of 0.89; and the test set R2 was 0.73 and RMSE was 3.15% for monitoring disease severity, compared to the optimal classification constructed using a single spectral index. The results may provide new ideas and methods for early and accurate monitoring of VW and other fungal diseases.<\/jats:p>","DOI":"10.3390\/rs14205241","type":"journal-article","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:34:30Z","timestamp":1666312470000},"page":"5241","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Early Monitoring of Cotton Verticillium Wilt by Leaf Multiple \u201cSymptom\u201d Characteristics"],"prefix":"10.3390","volume":"14","author":[{"given":"Mi","family":"Yang","sequence":"first","affiliation":[{"name":"Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi 832003, China"}]},{"given":"Changping","family":"Huang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Bejing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1203-8140","authenticated-orcid":false,"given":"Xiaoyan","family":"Kang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Bejing 100101, China"}]},{"given":"Shizhe","family":"Qin","sequence":"additional","affiliation":[{"name":"Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi 832003, China"}]},{"given":"Lulu","family":"Ma","sequence":"additional","affiliation":[{"name":"Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi 832003, China"}]},{"given":"Jin","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Bejing 100101, China"}]},{"given":"Xiaoting","family":"Zhou","sequence":"additional","affiliation":[{"name":"Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi 832003, China"}]},{"given":"Xin","family":"Lv","sequence":"additional","affiliation":[{"name":"Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi 832003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2318-8865","authenticated-orcid":false,"given":"Ze","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi 832003, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Song, R., Li, J., Xie, C., Jian, W., and Yang, X. 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