Abstract: The identification of crop disease is related to crop yield and quality, and it is an essential part in the development of intelligent agriculture. With the rapid development of deep learning in the field of image processing, the method of identifying crop disease types from images by deep learning has gradually become the mainstream. In this paper, we mainly review the methods of crop disease recognition based on deep learning, briefly introduce deep learning and convolutional neural network, and collect some common public disease image datasets. According to the different collection environment of training sample, we summarize the progress of deep learning-based disease identification methods in recent years from two aspects of laboratory and field, point out their advantages and disadvantages of each method, and conclude that there are three main problems in this research field such as insufficient data, difficult task and complex network structure of deep learning model. On this basis, we propose that the establishment of large-scale, multi-species, and multi-type disease database and the design of high-performance deep learning model are important development directions in the future.