Image Recognition of Apple Leaf Disease Based on Improved ResNet-50 and Transfer Learning
In order to solve the following problems,most of the apple leaf disease images obtained in natural scenes contain complex backgrounds.At the same time,because the shooting requirements are not so strict,the disease features are more likely to appear in any position in the image.All these factors will affect the recognition accuracy of convolutional neural networks,and its performance will be difficult to improve with the deepening of the network.An improved method of apple leaf disease identification based on ResNet-50 was proposed.First,the squeeze-and-excitation(SE)attention mechanism was introduced,and the residual module was improved to remove interference information such as complex background to a certain extent.On the other hand,the model's over-sensitivity to feature localization was reduced,so that the model can learn more important disease characteristics and reduce interference of complex background information.Finally,transfer learning training was used to accelerate the convergence of the model.The experimental results on the apple leaf disease dataset show that the accuracy of the improved model reaches 98.35％,which is 5％higher than that of ResNet-50.Compared with other traditional convolutional neural networks,this model has the advantages of faster convergence speed,higher recognition accuracy,better identification of apple leaf disease,and is suitable for apple leaf disease images obtained in natural scenes,and has strong practicability.
万方数据
维普
