关键词: 农作物叶片, 病害识别, 生成对抗网络, 神经网络, 微调

Abstract: When people use deep neural networks to classify images, they usually need a large number of training samples. However, it is difficult to obtain enough samples to ensure the training of neural network in practice. In order to solve this problem， this paper proposes an identification method based on generative adversarial network. The main idea is to train a sample generation model after modification of the existing GAN network model, then use neural network to identify the data set generated by the generation model, and finally use transfer learning method to fine-tune the neural network with real data. In order to verify the effectiveness of this method, five crop leaves (500 pieces per sample) are used for validation, the identification accuracy of plant leaves is more than 90%. The experimental results show that this method can improve the identification accuracy of the blade with a small number of samples and has strong universality.

Key words: crop leaf, diseases recognition, generative adversarial network, neural network, fine-tune