农业工程学报2012，Vol.28Issue(7)：161-167,7.DOI:10.3969/j.issn.1002-6819.2012.07.027
基于粗糙集和BP神经网络的棉花病害识别
Cotton diseases identification based on rough sets and BP neural network
摘要
Abstract
In order to improve the recognition rate of cotton diseases, an identification method of cotton diseases based on rough sets and BP neural network under natural environmental conditions was presented. In this method, Otsu method was used to get the threshold of//, a and b* components from four cotton diseases colored images in the HIS and/,*aV color spaces, and diseased regions of cotton were extract by intersection with H+a +b component and original image. Color moments and GLCM were used to extract texture features and color features from diseased regions. Features were then used as inputs to a cotton disease recognition model with rough set theory and a BP neural network classifier. The comparison test showed that rough set theory could cut down the dimension of features from sixteen to five and reduce training time of BP neural network to 25% of that without rough set, and the average recognition accuracy rate could reach up to 92.72%. The results of this study showed that the proposed classification method could accurately identify four cotton diseases, which can provide a technical support for cotton diseases prevention.关键词
棉花/病害/识别/粗糙集/BP神经网络Key words
cotton/diseases/identification/rough set/BP neural network分类
信息技术与安全科学引用本文复制引用
张建华,祁力钧,冀荣华,王虎,黄士凯,王沛..基于粗糙集和BP神经网络的棉花病害识别[J].农业工程学报,2012,28(7):161-167,7.基金项目
中央高校基本科研业务费专项资金资助(编号:KYCX2011072) （编号:KYCX2011072）
