Abstract: In order to solve the problem that the accuracy of deep neural network model learning is affected by the small sample of rice disease image dataset, an improved adversarial generative network model ViT-WGAN-GP (The Fusion of Vision Transformer and Wasserstein Generative Adversarial Networks with Gradient Penalty) is proposed for enhancing the image dataset. Firstly, the Vision Transformer structure is introduced in the generation model to enhance the learning of global features. Secondly, the WGAN-GP structure is used in the discrimination model to ensure the stability of the model training and improved the effect of the generated images through the Wasserstein measure function and the gradient penalty term. Finally, the enhanced sample set is used to train the deep neural network model. The experimental results show that the ViT-WGAN-GP model generates images with significant improvement compared with GAN and WGAN-GP. The average accuracy of rice disease recognition is 94. 3%,96. 2%, and 97. 5% for VGG16, ResNet34, which are improved by 9. 7%, 2. 8%, and 4.8%, respectively. The proposed ViT-WGAN-GP model can generate more realistic rice disease images and can improve the recognition accuracy of deep neural network models significantly with small sample sets.

Key words: image generation, vision transformer, wasserstein generative adversarial networks with gradient penalty(WGAN-GP), generative adversarial networks, rice diseases