Abstract: Aiming at the difficulty of image acquisition of maize disease, especially the problem of many different manifestations of gray spot disease, an image generation algorithm of corn gray spot disease was proposed based on cyclic uniform countermeasure network（CycleGAN）, which could make healthy crop images through the migration of disease images. This method extracted the healthy corn and the gray spot disease image features through feature extraction. Then, the two feature images were input into CycleGAN’s generator Gs. The residual network in the generator was combined to improve the accuracy of image transmission, and the two judges were used to judge whether the generated images were consistent. Finally, the desired corn gray leaf spot image was obtained by disease migration on the healthy corn image. The results showed that compared with VAE and GAN images, SSIM value increased 50.434% and 18.762% respectively, MSE value decreased 12.891% and 9.558%. In terms of visual effect, the effect of gray spot of corn with different disease degrees after CycleGAN migration was better, so the image of gray spot of corn generated by CycleGAN network was more accurate.