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The health of plants is directly related to the quality and quantity of agricultural products,therefore the disease detection of plants is an important research problem in agriculture. A multi-classification detection method based on kernel function support vector machine (SVM) was proposed for classification of healthy leaves and diseased leaves, and the detection of four diseases, including Alternaria alternata, Anthracnose, Bacterial Blight and Cercospora leaf spot. Because the image of diseased leaf was changeable, firstly, the contrast of diseased part and the healthy part was enhanced by the preprocessing,making the disease part more obvious. Then, leaf features were segmented and extracted on “a” and “b” component of the Lab color space. Using K-means clustering method, the clustering effect was enhanced. Finally, support vector machine (SVM) based on kernel function was used to identify and detect the four diseases. To improve the detection accuracy, 500 iterations were used to assess the maximum precision. Considering the influence of the coefficient of cross validation, 40% of the samples were used as validation data set, 60% were used as the training data set. Radial basis kernel function was adopted to carry out the training. In this method, traditional two kinds of leaf disease identification was extended to four kinds, and the experimental results proved the effectiveness of leaf classification of four kinds of diseases.And the recognition rate of the 4 diseases was the highest, reaching 89.5%, and the lowest was 70%.