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

摘要： 农作物病害的精准检测与识别是推动农业生产智能化与现代化发展的重要举措。随着计算机视觉技术的发展，深度学习方法已得到快速应用，利用卷积神经网络进行农作物病害检测与识别成为近年来研究的热点。基于传统农作物病害识别方法，分析传统方法的弊端所在；立足于农作物病害检测与识别的卷积神经网络模型结构，结合卷积神经网络模型发展和优化历程，针对卷积神经网络在农作物病害检测与识别的具体应用进行分类，从基于公开数据集和自建数据集的农作物病害分类识别、基于双阶段目标检测和单阶段目标检测的农作物病害目标检测以及国外和国内的农作物病害严重程度评估3个方面，对各类卷积神经网络模型研究进展进行综述，对其性能做了对比分析，指出了基于农作物病害检测与识别的卷积神经网络模型当前存在的问题有：公开数据集上识别效果良好的网络模型在自建复杂背景下的数据集上识别效果不理想；基于双阶段目标检测的农作物病害检测算法实时性差，不适于小目标的检测；基于单阶段目标检测的农作物病害检测算法在复杂背景下检测精度较低；复杂大田环境中农作物病害程度评估模型的精度较低。最后对未来研究方向进行了展望：如何获取高质量的农作物病害数据集；如何提升网络的泛化性能；如何提升大田环境中农作物监测性能；如何进行大面积植株受病的范围定位、病害严重程度的评估以及单枝植株的病害预警。

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