安徽农业科学2011，Vol.39Issue(28)：17580-17582,17732,4.
基于支持向量机的水稻纹枯病识别研究
Research on Recognition of Rhizocotonia solani Based on Support Vector Machine
摘要
Abstract
[Objective]The study aimed to research the automatic recognition of Rhkocotonia Solani by support vector machine (SVM) so as to make up the defect of artificial recognition and increase the accuracy and efficiency of recognition. [ Method] With R. Solani as the studied object, firstly,the method based on the vector median filtering was used to pre-treat the image of R. Solani,then the fuzzy c-mean clustering method was used to make for the gray image segmentation in the image segmentation stage and the feature parameters which represented the lesion were extracted from three aspects such as color,texture and shape,finally the SVM recognition method was used to identify Ft. Solani and was compared with the recognition method based on BP neural network. [ Result]Tlie SVM recognition method showed the recognition rate of 95.00% .which is better than that of BP neural network (91.88% ). [ Conclusion ] The recognition of R. Solani based on SVM could not only make up the defect of artificial recognition,but also increase the accuracy and efficiency of recognition,which showed the broad application prospects.关键词
水稻纹枯病/支持向量机/特征提取/分类识别Key words
Rhizocotonia solani/Support Vector Machine (SVM)/Feature extraction/ Classification and recognition分类
信息技术与安全科学引用本文复制引用
刘婷婷..基于支持向量机的水稻纹枯病识别研究[J].安徽农业科学,2011,39(28):17580-17582,17732,4.基金项目
国家863项目,“大田作物智能诊断技术系统研究与应用”(2007 AA 102237). （2007 AA 102237）
