Plants have high similarity and detail information in morphology, color, and texture. Traditional machine learning methods cannot meet the demands of feature extraction from many plants and they only recognize several types of plants, while deep learning can effectively deal with these difficulties, including amount, accuracy and speed of plant images recognition. This paper proposes a plant recognition algorithm based on the optimized P-AlexNet model. The traditional AlexNet mainly considered the classification of images with large difference between targets and ignored the difference of plant images. Therefore, more attention should be paid to distinguish deeper features when designing the network structure. By using inception module instead of traditional convolutional pooling single-channel structure, the proposed model can increase the representable range of underlying texture features. The green channel is separated from the remaining two channels such that the extracted features can characterize the information of leaf texture and the structure of the flower to attain better interpretability of the network. The contrastiveloss function in the siamese network is utilized to improve the recognition accuracy among plant categories after full connection layer. Based on the AlexNet network model in CNN, the generalization and the characterization of the detail features and the recognition precision of the model can be effectively improved. The concept of migration learning is employed to update the plant identification and GPU parallel computing is adopted to speed up model training and image recognition speed. The model training uses an image dataset with 206 plants, composed of Oxford102 and Ecust104 dataset, and the validation accuracy of the model is 86.7%. Based on this proposed model, this paper further develops a platform for intelligent plant image recognition, including Web site and App application of Android and IOS. It is shown from these experiments that the average detection time is 1.282 s and the higher accuracy and generalization and fast recognition speed can be attained.