【Purposes】 At present, tomato disease recognition based on convolutional neural network relies on a large amount of labeled data, and the lack of data samples is an important problem affecting the accuracy of tomato disease recognition. 【Methods】 Therefore, in order to obtain enough tomato leaf disease images and improve the accuracy of tomato disease recognition, a new data augmentation method HAM_ACGAN (Hidden parameter label and Attention attached Multi scale ACGAN)based on Generative Adversarial Network (GAN)is proposed. On the basis of with auxiliary classifiers, in order to supplement the intra-class information, the hidden variable is connected to the input noise to control the generation of different classes of diseases on the leaves; at the same time, a generator with residual attention block is designed to capture the disease information in the leaves to generate tomato leaves with obvious disease features; finally, a multi-scale discriminator is used to enrich the detail texture of the generated images. 【Conclusions】 The experimental results show that the proposed data enhancement method can generate tomato leaves with obvious disease features, which can meet the large data amount requirement for neural network training, thereby it can improves the recognition accuracy of the disease recognition network.