文献类型： 外文期刊

作者： Li, Ze ¹ ; Huang, Hong-Xing ¹ ; Zheng, Ye-Lu ¹ ; Wang, Zhou-Yuan ¹ ;

作者机构： 1.Sch Informat Sci & Technol, Sci Tech Informat Inst, GAAS, Guangzhou, Peoples R China

关键词： rough set; Intelligent Diagnosis; Inference

期刊名称：FIFTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2012): ALGORITHMS, PATTERN RECOGNITION AND BASIC TECHNOLOGIES

ISSN： 0277-786X

年卷期： 2013 年 8784 卷

页码：

收录情况： SCI

摘要： Along with the popularity of computer and rapid development of information technology, how to increase the accuracy of the agricultural diagnosis becomes a difficult problem of popularizing the agricultural expert system. Analyzing existing research, baseing on the knowledge acquisition technology of rough set theory, towards great sample data, we put forward a intelligent diagnosis model. Extract rough set decision table from the samples property, use decision table to categorize the inference relation, acquire property rules related to inference diagnosis, through the means of rough set knowledge reasoning algorithm to realize intelligent diagnosis. Finally, we validate this diagnosis model by experiments. Introduce the rough set theory to provide the agricultural expert system of great sample data a effective diagnosis model.