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

作　　者：胡维炜 张武 刘连忠[1,2] HU Weiwei;ZHANG Wu;LIU Lianzhong(Key Laboratory of Technology Integration and Application in Agricultural Internet of Things,Ministry of Agriculture,P.R.China,Hefei,Anhui,230036,China;Information and Computer Science College of Anhui Agricultural University,Hefei,Anhui,230036,China)

机构地区：[1]农业部农业物联网技术集成与应用重点实验室,安徽合肥230036 [2]安徽农业大学信息与计算机学院,安徽合肥230036

出　　处：《湖南农业大学学报（自然科学版）》2018年第2期225-228,共4页Journal of Hunan Agricultural University(Natural Sciences)

基　　金：农业部引进国际先进科学技术948项目(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);安徽农业大学引进与稳定人才项目(wd2015–05)

摘　　要：利用中值滤波结合k均值聚类的方法分割出小麦白粉病、条锈病和叶锈病叶部病斑,分别采用颜色矩和灰度共生矩阵的方法提取病斑的颜色特征和纹理特征参数,设计了一种基于Variance算法初选与序列浮动前向选择搜索算法(SFFS)相结合的特征选择方法,选择出优良的特征子集,实现对小麦3种叶部病害的识别。试验以SVM为分类器,利用特征选择方法获得的特征子集识别准确率为99%,与采用主成分分析(PCA)方法进行特征降维获得的子集的识别准确率比较,能有效降低特征维度,提高识别准确率。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 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]