in second step, some state-of-the-art color and texture features are extracted from the segmented image, and finally, fruit diseases are classified using a Multi-class Support Vector Machine. We show the significance of using clustering technique for the disease segmentation and Multi-class Support Vector Machine as a classifier for the automatic classification of fruit diseases. In order to validate the proposed approach, we have considered three types of the diseases in apple; apple blotch, apple rot and apple scab. The experimental results shows that the proposed approach can significantly achieve accurate detection and automatic classification of fruit diseases.