Segmentation of color images of grape diseases using K_means clustering algorithm

To improve the segmentation precision and effectiveness of plant disease images, a kind of unsupervised segmentation processing method based on K_means clustering (HCM) algorithm was proposed according to the properties of the symptoms and images of plant diseases. On the basis of the color differences of ab two-dimension data space from L*a*b* color space model, iterative color clustering of two clusters was conducted using squared Euclidian distance as the similarity distance and mean square deviation as the clustering criterion function. And the mathematics morphology algorithm was used to correct the clustering results. The proposed method was used to segment the color images of three kinds of grape diseases. The results show that it can satisfactorily segment the diseased regions from the color images of grape diseases with good robustness and good accuracy.