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In this paper, we propose a method for identification and classification of fruit diseases. Initially, the fruit image is segmented using K-means and C-Means clustering algorithms, later performance evaluation of segmentation algorithm is conducted using the parameters such as Measure of under-segmentation (MUS), Measure of overlapping (MOL), Dice similarity measure (DSM), Measure of over segmentation (MOS), Error-rate (ER). The performance evaluation of segmentation parameters are calculated based on the comparison between the manually segmented ground truth and segmentation result generated by the image segmentation approaches. After segmentation, features are extracted using Gray Level Co-occurrence Matrix. The K-Nearest Neighbors Algorithm classifier is used to classification. The collected database consists of 10 classes with 243 images. The result of classification has relatively …