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Pomegranate is one of the widely grown fruit crops in Lintong, Shaanxi. The productivity of pomegranate is reduced by infection caused by various types of diseases in its fruit, stem and leaves, and leaf diseases are mainly caused by bacteria, fungi, viruses and so on. Disease is a major factor limiting fruit yield, and disease is often difficult to control, and without an accurate diagnosis of disease, appropriate control action cannot be taken at the appropriate time. Image processing technology is one of the widely used techniques in plant leaf disease detection and classification, aiming at using support vector machine classification technology to detect and classify pomegranate leaf disease. Firstly, K-means clustering method was used to segment the lesion area domain, and then extract the color and texture features. Finally, the LSVM (Linear Support Vector Machine) classification technique was used to detect the leaf disease types. The proposed system can successfully detect and classify the examined diseases with an accuracy of 89.55%.