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A Predictive Fuzzy Expert System for Crop Disease Diagnostic and Decision Support

A Predictive Fuzzy Expert System for Crop Disease Diagnostic and Decision Support

Prateek Pandey, Ratnesh Litoriya
Copyright: © 2020 |Pages: 20
ISBN13: 9781522591757|ISBN10: 1522591753|ISBN13 Softcover: 9781522591764|EISBN13: 9781522591771
DOI: 10.4018/978-1-5225-9175-7.ch010
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MLA

Pandey, Prateek, and Ratnesh Litoriya. "A Predictive Fuzzy Expert System for Crop Disease Diagnostic and Decision Support." Fuzzy Expert Systems and Applications in Agricultural Diagnosis, edited by A.V. Senthil Kumar and M. Kalpana, IGI Global, 2020, pp. 175-194. https://doi.org/10.4018/978-1-5225-9175-7.ch010

APA

Pandey, P. & Litoriya, R. (2020). A Predictive Fuzzy Expert System for Crop Disease Diagnostic and Decision Support. In A. Kumar & M. Kalpana (Eds.), Fuzzy Expert Systems and Applications in Agricultural Diagnosis (pp. 175-194). IGI Global. https://doi.org/10.4018/978-1-5225-9175-7.ch010

Chicago

Pandey, Prateek, and Ratnesh Litoriya. "A Predictive Fuzzy Expert System for Crop Disease Diagnostic and Decision Support." In Fuzzy Expert Systems and Applications in Agricultural Diagnosis, edited by A.V. Senthil Kumar and M. Kalpana, 175-194. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-5225-9175-7.ch010

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Abstract

Soybean accounts for 38% of the total oilseed production in India, and around 50% of the total oilseed production in Kharif season. This crop has shown tremendous growth over the last four decades with an average national yield of 1264 kg/hectare. Currently, soybean is severely attacked by more than 10 major diseases. Yield losses due to different diseases ranges from 20 to 100%. Timely detection of soybean crop disease would help farmers save their money, effort, and crop from being destroyed. This chapter presents a case study on the development of a decision support system for prediction of soybean crop disease severity. The outcome of this system will aid farmers to decide the extent of disease treatment to be employed. Such predictions make use of human involvement, and thus are a source of ambiguities. To deal with such ambiguities in decision making, this decision support system uses fuzzy inference method based on triangular fuzzy sets.

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