Study on the Agricultural Expert System of Similarity Rough Set Optimizing Case Reasoning

In order to solve the problems caused by the shortage of subjectively preset case weight coefficient in the traditional case reasoning agricultural expert system and thus improve the accuracy and intellectualization of diagnosis and prevention of the system, an agricultural expert system of similarity-rough set optimizing case reasoning is constructed with the introduction of the attributes of similarity-rough set reducing case redundancy, this study focuses on the optimizing way of the case reasoning, the system structure design for data access based on container-managed persistence mode, and the key technology of system functional mode and realization. Practices prove that this system can effectively resolve the problems of attribute redundancy in the plant diseases and insect pests case data, get rid of the noise inference, and simplify the case base, so as to improve the ability to diagnose and treat plant diseases and get easier access to maintenance and development.