Research on plant disease identification based on transfer learning and convolutional vision transformer

The plant diseases impact on both the food production and quality in the agriculture sector. As the problem that traditional plant disease identification methods rely on training sample data and manual extraction of features, it is difficult to identify in the field environment and the classification accuracy is not high. Based on transfer learning, this study proposes a Convolutional Vision Transformer（CViT） model that preprocesses image sub-blocks with convolution operations for plant disease identification. On the basis of the Vision Transformer（ViT） model structure, the convolution operation is introduced to preprocess input images, the convolution operation improves the richness of the low-level features, and then in the ViT learning process, the multi-head mechanism is used to increase the weight of useful features and noise suppression. So as to achieve the purpose of improving the feature learning ability and enhancing the robustness of the model. The experimental results show that using the transfer learning method on the ibean dataset can improve the recognition accuracy of the model by more than 10%. Applying transfer learning to the CViT model achieves a recognition accuracy of 98.12% on the ibean dataset, an improvement of about 2%. The recognition accuracy in the PlantVillage dataset is 99.91%. The proposed recognition method has high recognition accuracy and robustness under complex background interference, and can meet the requirements of plant disease identification under natural conditions.