Research on apple leaf disease image recognition based on convolutional neural network
In order to solve the problem of low efficiency and low accuracy of apple leaf disease recognition,a study on apple leaf disease recognition model was launched.Four types of images of apple black rot,apple black star,apple rust and apple healthy leaves were obtained by collecting and organizing the network public dataset,1 200 images were randomly selected to build the datasets,and data enhancement was used to pre-process the data to improve the robustness of the model and reduce the influence of other factors on the model recognition.Combining transfer learning with convolutional neural networks,on the basis of four pre-trained models,InceptionV3,Xception,NasNetmobile,and Inception-ResNetV2,the fully connected layer is abandoned,and the global average pooling layer is used to complete the feature integration,while the models are fine-tuned to compare the recognition effects of each model.The training comparison results of the eight models before and after fine-tuning show that the fine-tuned Inception-ResNetV2 model can achieve 98.83％accuracy on the test set,which can accurately and quickly identify the disease types and provide a reference for related disease identification.
apple leaf diseasetransfer learningconvolutional neural networkimage recognition
万方数据
维普
