Identification Method of Grape Leaf Diseases Based on Improved CCT Model

Abstract

Grape is an important cash crop that is susceptible to diseases when growing, resulting in lower yield and quality. In recent years, transformers have achieved excellent performance in a variety of natural language processing and image recognition tasks through the self-attention mechanism. Therefore, this paper proposes a grape leaf disease recognition model named Dense Convolutional Transformer (DensCT). The compact convolutional transformer (CCT) is used as the backbone in this model, which improves the convolutional module of the original model by introducing densely connected modules, enhancing the transfer and reuse of features between networks. This also modifies the single-scale feature extraction method of the original model to multi-scale, which improves the feature extraction performance. Finally, the model was trained on two small-scale datasets from scratch, and the recognition accuracy of the final model on the test sets reached 89.19% and 93.92%. Compared with CCT, DenseNet121, ResNet50, MobileNetV3 and ViT, the recognition accuracy improved by 4.73%, 3.38%, 10.81%, 0.68% and 18.24% on the first dataset and 6.08%, 5.41%, 1.35%, 3.38% and 12.84% on the second dataset. The experimental results show that the proposed model can effectively identify grape leaf diseases, which can provide a reference for building disease leaf recognition models on small-scale datasets.