基于SE模块和ResNet的番茄病虫害识别方法

番茄病虫害是引起番茄减产的重要因素。精确识别病虫害种类是当前国际热点问题之一，有助于及时有效采取针对性的病虫防治办法，减少和避免因番茄减产导致的经济损失。针对传统虫害识别方法存在效率和精确率低的问题，利用Kaggle网站上的Tomato数据集，构建基于压缩和激励（SE）模块的深度残差网络模型（ResNet），优化番茄病虫害识别方法。结果表明:通过Pytorch框架下的迁移学习，改进后的网络模型对番茄病虫害图像的平均识别准确率最高为97.96%；基于SE模块的ResNet网络模型有助于增强特征区分能力，增加模型的通用性和鲁棒性。研究结果对番茄病虫害的及时监测和处理、提高番茄产量具有重要意义。 

Identification Method of Tomato Diseases and Pests Based on SE Module and ResNet

Tomato diseases and pests are leading factors to cause decline of tomato production． Identifying types of diseases and pests accurately is one of current international hot issues, which helps to take timely and effective measures to control diseases and pests, and reduce and avoid economic losses caused by decline of tomato production． Aiming at problems of low efficiency and accuracy of traditional diseaese and pests identification methods, tomato data set on Kaggle website were used to build a deep residual network model（ResNet）based on Squeeze-and-Excitation（SE）module to optimize tomato pests diseases and identification method． Results showed that: through transfer learning under Pytorch framework, average recognition accuracy of improved network model for tomato pests and diseases images was as high as 97.96%; ResNet network model based on SE module helped to enhance ability of distinguishing features, which increased universality and robustness of the model． Results of this study were of great significance for timely monitoring and treatment of tomato diseases and pests and increase of tomato production．