Sample Expansion and Classification Model of Maize Leaf Diseases Based on the Self-Attention CycleGAN

Abstract

In order to address the limited scale and insufficient diversity of research datasets for maize leaf diseases, this study proposes a maize disease image generation algorithm based on the cycle generative adversarial network (CycleGAN). With the disease image transfer method, healthy maize images can be transformed into diseased crop images. To improve the accuracy of the generated data, the category activation mapping attention mechanism is integrated into the original CycleGAN generator and discriminator, and a feature recombination loss function is constructed in the discriminator. In addition, the minimum absolute error is used to calculate the differences between the hidden layer feature representations, and backpropagation is employed to enhance the contour information of the generated images. To demonstrate the effectiveness of this method, the improved CycleGAN algorithm is used to transform healthy maize leaf images. Evaluation metrics, such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Fréchet inception distance (FID), and grayscale histogram can prove that the obtained maize leaf disease images perform better in terms of background and detail preservation. Furthermore, using this method, the original CycleGAN method, and the Pix2Pix method, the dataset is expanded, and a recognition network is used to perform classification tasks on different datasets. The dataset generated by this method achieves the best performance in the classification tasks, with an average accuracy rate of over 91%. These experiments indicate the feasibility of this model in generating high-quality maize disease leaf images. It not only addresses the limitation of existing maize disease datasets but also improves the accuracy of maize disease recognition in small-sample maize leaf disease classification tasks.