关键词: 深度学习, 卷积神经网络, 模型结构, 病害识别, 病害检测

Abstract: Accurate detection and identification of crop diseases is an important measure to promote the development of intelligent and modernized agricultural production. With the development of computer vision technology, deep learning methods have been rapidly applied, and the use of convolutional neural networks to detect and identify crop diseases has become a hot research topic in recent years. In this paper, the disadvantages of traditional crop disease identification methods are analyzed. Based on the convolutional neural network model structure of crop disease detection and recognition, combined with the development and optimization process of convolutional neural network model, the specific application of convolutional neural network in crop disease detection and recognition is classified. From based on public data sets and selfbuilt data set of crop disease classification; based on double stage of target detection and single phase detection of crop disease detection; foreign and domestic crop disease severity evaluation of three aspects, the research progress of convolutional neural network model are summarized, while also contrasted its performance. The current problems of convolutional neural network model based on crop disease detection and recognition are pointed out: the network model with good recognition performance on public data sets has poor recognition performance on selfbuilt complex data sets. The crop disease detection algorithm based on twostage target detection has poor realtime performance and is not suitable for small target detection; The detection accuracy of crop disease based on singlestage target detection is low in complex background. The accuracy of crop disease degree assessment model in complex field environment is low. Finally, the future research directions are prospected as follows: how to obtain highquality crop disease data sets; how to improve the network generalization performance; how to improve crop monitoring performance in field environment; how to locate the area of large area crop disease, and to evaluate the severity of disease and early warning of single branch crop disease.

Key words: deep learning, convolutional neural network, model structure, disease identification, disease detection