关键词: 小麦病害, Vision Transformer, 迁移学习, 图像识别, 数据增强

Abstract: Wheat powdery mildew, head blight, and rust are the three major diseases that harm wheat yield. In order to improve the recognition accuracy of wheat disease images, a wheat disease image recognition algorithm based on Vision Transformer was proposed. Firstly, the images of wheat diseases, including wheat powdery mildew, scab, and rust, were collected by field shooting, and the original images were preprocessed to establish the wheat disease image recognition data set. Then, the wheat disease image recognition algorithm was constructed based on the improved Vision Transformer, analyzing the influence of different transfer learning methods and data enhancement on the model identification effect. The experiments showed  that full parameter transfer learning and data enhancement could significantly improve the convergence speed and identification accuracy of the Vision Transformer model. Finally, the performance of Vision Transformer, AlexNet and VGG 16 algorithms on the same dataset was compared under the same time condition. The experimental results showed that the average recognition accuracy of the Vision Transformer model for the three wheat disease images was 96.81%, which was 6.68% and 4.94% higher than that of  AlexNet and VGG 16 models, respectively.

Key words: wheat disease, Vision Transformer, transfer learning, image recognition, data augmentation