Clivia Plant Diseases And Pests’ Diagnosis Model Building And The Realization Of Intelligent Expert System

This paper focuses on clivia the rapid diagnosis of diseases and pests,which combines with object-oriented programming technology, database technology. Based on Visual Studio2010 platform, the database technology and expert system has been used. In addition, the clivia expert system has been developed, which can realize the image recognition and diagnosis of clivia diseases and pests. The following progress has been achieved:1. Established clivia diseases and pests database. In this paper, we have collected 15 kinds of clivia information of common diseases and pests in our country. With the Access database platform to build clivia pest database,creating favorable conditions for the information query,retrieve and display of clivia plant diseases and pests.2. Study on the knowledge representation method based on framework and rules. By communication with domain experts, collecting and sorting out the relevant information of clivia diseases and pests, based on the knowledge representation method based on the framework and rules, the corresponding knowledge base has been set up, and it has paved the way for the system development.3. The expert system of clivia diseases and pests diagnosis has been developed. Combine the artificial intelligence technology, multimedia technology and object-oriented technology,aiming at the actual demand for flower production, for the purpose of solve problem of production practice, using rule-based knowledge representation and forward reasoning technology, developed the expert system of clivia diseases and pests diagnosis. The expert system contains diagnostic module, query module and browsing module.4. This paper introduces the traditional plant diseases and insect pests recognition based on digital image processing technology, in terms of color feature extraction, extract the image of the R, G, B three channel color moment features respectively, extract the first moment and the second moment of the three channels. Extract the quantity, entropy and inertia moment of the texture feature as the important features of diseases and pests image recognition and diagnosis.Using these nine features building a feature vector, then use the SVM classifier classifies three kinds of diseases and pests of clivia. Finally integrate SVM classification model and expert system.