{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T15:53:37Z","timestamp":1774799617112,"version":"3.50.1"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,8,5]],"date-time":"2020-08-05T00:00:00Z","timestamp":1596585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["61802411"],"award-info":[{"award-number":["61802411"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,2,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In this study, a cotton disease diagnosis method that uses a combined algorithm of case-based reasoning (CBR) and fuzzy logic was designed and implemented. It focuses on the prevention, diagnosis and control of diseases affecting cotton production in China. Conventional methods of disease diagnosis are primarily based on CBR with reference to user-provided symptoms; however, in most cases, user-provided symptoms do not fully meet the requirements of CBR. To address this problem, fuzzy logic is incorporated into CBR to allow for more flexible and accurate models. With the help of CBR and fuzzy reasoning, three diagnostic results can be obtained by the cotton disease diagnosis system (CDDS) constructed in this study: success, success but not exact and failure. To verify the reliability of the CDDS and its ability to diagnose cotton diseases, its diagnostic accuracy and stability were analyzed and compared with the results obtained by the traditional expert scoring method. The analysis results reveal that the CDDS can achieve a high diagnostic success rate (above 90%) and better diagnostic stability than the traditional expert scoring method when at least four disease symptoms are input. The CDDS provides an independent and objective source of information to assist farmers in the diagnosis and prevention of cotton diseases.<\/jats:p>","DOI":"10.1093\/comjnl\/bxaa098","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T19:10:53Z","timestamp":1594062653000},"page":"155-168","source":"Crossref","is-referenced-by-count":12,"title":["A Cotton Disease Diagnosis Method Using a Combined Algorithm of Case-Based Reasoning and Fuzzy Logic"],"prefix":"10.1093","volume":"64","author":[{"given":"Yuhong","family":"Dong","sequence":"first","affiliation":[{"name":"Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, 17 Qinghua East Road, Beijing 100083, PR China"}]},{"given":"Zetian","family":"Fu","sequence":"additional","affiliation":[{"name":"Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, 17 Qinghua East Road, Beijing 100083, PR China"}]},{"given":"Stevan","family":"Stankovski","sequence":"additional","affiliation":[{"name":"Faculty of Technical Sciences, University of Novi Sad, Dr Zorana Dindica 1, Novi Sad 21000, Serbia"}]},{"given":"Yaoqi","family":"Peng","sequence":"additional","affiliation":[{"name":"Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, 17 Qinghua East Road, Beijing 100083, PR China"}]},{"given":"Xinxing","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University,17 Qinghua East Road, Beijing 100083, PR China"}]}],"member":"286","published-online":{"date-parts":[[2020,8,5]]},"reference":[{"key":"2021080500102442000_ref1","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1186\/s12859-019-3262-y","article-title":"Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network","volume":"20","author":"Xiao","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2021080500102442000_ref2","first-page":"342","volume-title":"Recent Trends in Image Processing and Pattern Recognition (RTIP2R)","author":"Randive","year":"2018"},{"key":"2021080500102442000_ref3","first-page":"15","volume-title":"4th Int. 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