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

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.