基于GLCM纹理特征提取的黄瓜叶部病害检测算法研究

Research on cucumber leaf disease detection algorithm based on GLCM texture feature extraction

针对传统的植物叶部病害检测算法复杂的特点,提出了一种基于GLCM纹理特征提取的植物叶部病害检测算法。以黄瓜叶部炭疽病为研究对象,利用K-means聚类算法进行图像阈值分割,并利用灰度共生矩阵提取样本的能量均值、熵均值、对比度均值和相关均值等4种纹理特征参数,通过参数训练,确定无病害区和有病害区参数的区域,进而判定样本的病害情况。结果表明该算法实现效率高、鲁棒性较好。

To the complex algorithm of traditional plant leaf disease detection,this paper proposed a plant leaf disease detection algorithm based on GLCM texture feature extraction. As the research object of cucumber leaf anthracnose,the K-means clustering algorithm was used to perform image threshold segmentation,and the gray level co-occurrence matrix was used to extract the energy mean,entropy mean,contrast mean and correlation mean of the sample. With the parameter training,the area of disease-free area and diseased area parameters were determined,and then the disease condition of the sample was judged. The results showed that the algorithm had high efficiency and good robustness.