Abstract: Abstract: Crop diseases often seriously affect both the quality and quantity of agricultural products and cause economic losses to farmers. How to accurately and quickly recognize the crop disease information is an important problem in preventing and controlling crop diseases. Crop disease recognition by crop leaf symptoms is a basic method of attempting to address this problem. Studies show that relying on pure naked-eye observing of the leaf symptoms by experts to detect the crop diseases can be prohibitively expensive, especially in developing countries. Automatic detection of crop diseases is an essential research topic, as it may prove benefits in monitoring large fields of crops, and thus automatically detect the symptoms of diseases as soon as they appear on crop leaves. In a research study of identifying and diagnosing crop diseases, the pattern of the disease is important part. Leaf spots are considered the important units indicating the existence of disease and regarded as indicators of crop diseases. A technique to detect the disease spot is needed. It is important to select a threshold of gray level for extracting the disease spot from the crop leaf. In order to classify disease leaf sample categories, a set of spot features for the classification and detection of the different disease leaves are required. The disease leaf images of the crop would be processed by using a series of image pre-processing methods, such as image transforming, image smoothing, and image segmentation. In this paper, crop disease leaf spots were segmented by the seeded region growing based region algorithm. Because the crop leaves look differ in many ways, most of classical pattern recognition methods are not effective to extract the disease features and reduce the dimensionality of diseased leaf images. A novel manifold learning algorithm called local discriminant projects (LDP) was proposed and was applied to crop disease recognition. After being projected into a low-dimensional subspace, the data points in the same class were close to each other, whereas the gaps between the data points from different classes became wider than before. In LDP, the class action was introduced to construct the objection function. There was no need to calculate the inverse matrix, so the small sample size problem occurring in traditional linear discriminant analysis was naturedly avoided, and much computational time would be saved by using LDP for dimensionality reduction. After each spot image was reorganized as one-dimensionality vector and its dimensionality was reduced by LDP, the nearest neighbor classifier was adopted to recognize crop disease. The extensive experiments were performed on a real maize disease leaf image database and compared with the traditional disease recognition methods and the supervised subspace learning algorithms in recognition performance. The mean correct classification rate of the proposed method was 94.4%. The proposed method was compared with the classical crop disease recognition methods (ANN, PCA+PNN, and Bayesian) and supervised subspace algorithms (LDE, DNE). The experiment results showed that the proposed method was effective and feasible for crop disease recognition. The preliminary study showed that there is a potential to establish an online field application in crop leaf disease detection based on leaf image processing techniques.