湖南农业大学学报（自然科学版）2018，Vol.44Issue(2)：225-228,4.DOI:10.13331/j.cnki.jhau.2018.02.021
基于Variance-SFFS的小麦叶部病害图像识别
Identification of wheat leaf diseases based on Variance–SFFS algorithm
摘要
Abstract
Median Filter Algorithm combined with K–means clustering was employed to segment lesion area of wheat powdery mildew, stripe rust and leaf rust. Color moments and gray–level co–occurrence matrix (GLCM) were used to extract color features and texture features. Variance algorithm and sequential floating forward search (SFFS) algorithm were used for selection of optimal feature subset with which classification and recognition of the 3 kind of wheat diseases were achieved. Experiment was done based on SVM using the feature subset, and the classification accuracy was up to 99%. Compared with PCA method which classifying feature subset obtained by dimension reduction, the method used in this study could reduce the feature space and improve recognition accuracy effectively.关键词
小麦病害/特征降维/启发式搜索/支持向量机Key words
wheat disease/dimension reduction/heuristic search/support vector machine分类
信息技术与安全科学引用本文复制引用
胡维炜,张武,刘连忠..基于Variance-SFFS的小麦叶部病害图像识别[J].湖南农业大学学报（自然科学版）,2018,44(2):225-228,4.基金项目
农业部引进国际先进科学技术948项目(2015–Z44) （2015–Z44）
农业部农业物联网技术集成与应用重点实验室开放基金项目(2016KL05) （2016KL05）
安徽农业大学引进与稳定人才项目(wd2015–05) （wd2015–05）
