Abstract: Abstract: Study on the classification and recognition methods of plant species by using plant leaf images plays an important role in protecting plant species and ecological environment. Designing a computer-aided plant recognition system is necessary and useful since it can facilitate plants recognition and classification, and understanding and managing plant species. Compared with other plant recognition methods, such as cell and molecule biology methods, plant recognition and classification based on leaf image processing is becoming a popular trend. In protecting plant perspective, leaf images have been used by plant protection researchers to diagnose plant diseases and this method has been proven to be reliable for years. Each kind of plant leaf has its own features and carries large significant information that can be used to recognize and classify the origin or the type of plant. Leaf shape is a prominent feature that most people use to recognize and classify a plant. The features, such as leaf area, perimeter, diameter, physiological length and physiological width, are basic geometry information that can be extracted from the leaf shape. In addition, leaf color, textures and vein pattern are also considered as important classifying features. All these features are useful for recognizing and classifying plant. Because of the complex and diversity of plant leaf images and the differences between within-class leaf images, many classification and recognition methods that use color, shape and texture of the leaves cannot meet the need of the plant automatic identification system. The feature extraction and dimensional reduction is a key step to plant classification. The classical linear dimensional reduction methods can not effectively applied to leaf image processing because the plant leaf images are general nonlinear data. Manifold learning based recognition methods have been successfully applied to face recognition. Based on manifold learning, a probability locality preserving discriminant projections (PLPDP) method was proposed for plant recognition in this study. The method was to learn a linear transformation, and focuses only on the pairwise points where the two points were neighbors to each other. First, the classification probability of each sample was computed, and the weighted matrix was defined by local information, label information and classifying probability. Then objective function was redefined. Then, through minimizing the function, an optimal projection matrix could be preserved in the low-dimensional feature space, such as the distribution information contained in the original data. Compared with the popular dimensionality reduction methods, such as linear discriminant analysis (LDA), locality preserving projections (LPP), discriminant LPP (DLPP) and supervised LPP(SLPP), the proposed method took three kinds of information into account (i.e., local information, label information and classifying probability) and refined the weighted matrix, which not only contained neighborhood information of samples, but also could reflect the probability from which a sample was correctly classified when its K-nearest-neighbors were selected. After projection, the neighborhood relationship of the intra-class samples which possessed more classification probability could be preserved, and the considered pairwise points in within-class were as close as possible, while those in between different classes were as far as possible, in which the global optimum could be effectively obtained. Finally, the experimental results on the public plant leaf image database demonstrate the superiority of the proposed algorithm compared with other algorithms.