1. A Cotton Disease Diagnosis Method Using a Combined Algorithm of Case-Based Reasoning and Fuzzy Logic
- Author
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Stevan Stankovski, Peng Yaoqi, Dong Yuhong, Zetian Fu, and Xinxing Li
- Subjects
General Computer Science ,010201 computation theory & mathematics ,Computer science ,business.industry ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Case-based reasoning ,0102 computer and information sciences ,02 engineering and technology ,Artificial intelligence ,business ,01 natural sciences ,Fuzzy logic - Abstract
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.
- Published
- 2020