基于对比学习的番茄叶片病害识别研究OA

In view of the current situation that traditional Deep Learning models exhibit low recognition accuracy and limited generalization ability in the process of tomato leaf disease recognition,this paper proposes a recognition model based on the Supervised Contrastive Learning method.The model proposes the AMECA module based on Channel Attention,which effectively captures the dependencies among channels,enhances the model's channel fusion ability,and improves the recognition performance.The AMECA module is integrated into ResNet18 model as an image feature extractor,and a high-precision tomato leaf disease recognition model is trained through the Supervised Contrastive Learning method.The experimental results on the tomato leaf disease dataset show that the accuracy of the model reaches 99.198%,which is 3.208%higher than that of the original ResNet18 model.Compared with some other traditional Convolutional Neural Networks,it has higher recognition accuracy and can better recognize tomato leaf diseases.It is applicable to tomato leaf images obtained in natural scenes,and demonstrates strong practicability.