Abstract: In order to achieve the rapid diagnosis of rice leaf blast, authors utilized image processing techniques and neural network comprehensively for the recognition of leaf blast lesion. For the accuracy rate of comparative analysis on lesion identification, three multilayer perception classifiers were designed. Three features of normal and lesion part which were texture, color and combined feature of texture and color were selected as input unit for different classifier respectively. Output layer adopted one unit for the identify results of lesion and normal region. First of all, grayscale image was transformed from RGB image, using gray level co-occurrence matrix to extract energy, contrast, entropy, inverse gap as texture features from leaf lesion region and normal region respectively. The RGB color space was transformed to HIS and Lab space, and L, a, b values were extracted as color features from lesion area and normal region respectively, using different BP neural network classifier to identify lesion region.120 images were selected as test objects. The experimental results showed that if the combined feature of color and texture was used as input parameters, the accuracy rate would be 10%-15% higher than that of texture features and color features alone. The results of this paper laid a foundation for realization of the automatic diagnosis of rice diseases.