关键词: 纹理特征, 灰度共生矩阵, 聚类算法, 图像分割, 植物叶部病害

Abstract: 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.

Key words: texture feature, gray level co-occurrence matrix, clustering algorithm, image segmentation, plant leaf disease