Segmentation of tomato leaf images based on adaptive clustering number of K-means algorithm

In image-based intelligent identification of crop diseases, leaf image segmentation is a key step. Although the K-means is a commonly used algorithm between a number of segmented methods, which needs to set the clustering number in advance, so as to make a manual influence on the image segmentation quality. This paper studies an improved K-means algorithm based on the adaptive clustering number for the segmentation of tomato leaf images. The whole experiment images were acquired from the tomato we grew. The white paper background images were used for designing algorithm and the natural background images were the algorithm validated data. Through a series of pretreatment experiments, the value of the clustering number in this algorithm was automatically determined by calculating the DaviesBouldin index, and the initial clustering center was given to prevent the clustering calculation from falling into a local optimum. Finally, we verified the accuracy of segmentation by two kinds of objective assessment measures, the clustering F1 measure and Entropy. Compared with the traditional K-means algorithm, DBSCAN algorithm, Mean Shift algorithm and ExG-ExR color indices method, the proposed algorithm can successfully segment the tomato leaf images more precisely and efficiently.