Tomato disease control is of great significance to ensure crop production and tomato disease classification study is an essential tool for doing so. In this paper, we propose a new data augmentation method based on deep threshold multi-feature extraction convolution GAN with Mixed Attention and Markovian Discriminator (MMDGAN) for tomato disease leaf classification. Firstly, in the generator of MMDGAN, a deep threshold multi-feature extraction module was proposed to improve the feature extraction of tomato disease leaves. Then, a mixed attention mechanism combined cross attention module with fused features-highlighting module was proposed to coordinate the overall generation of images. Finally, for the discriminator, Markov discriminator was used to strengthen the similarity judgment of local texture of images. Based on the open datasets PlantVillage, the Frechet Inception Distance (FID) score of healthy tomato leaf image, Leaf Mold, Leaf Curl and Spider Mite generated by MMDGAN were 159.3010, 164.4744, 230.3825 and 254.9866 respectively. Thereafter, a B-ARNet model is trained on synthetic and real images using transfer learning to classify the four categories of tomato diseases. The proposed method achieved an accuracy of 97.12%, with and F1 value of 97.78%. The proposed approach shows its superiority over the existing methodologies.

番茄病害防治对保证作物生产具有重要意义，番茄病害分类研究是实现这一目标的重要工具。在本文中，我们提出了一种基于深度阈值多特征提取卷积 GAN 与混合注意和马尔可夫判别器 (MMDGAN) 的新数据增强方法，用于番茄病叶分类。首先，在MMDGAN的生成器中，提出了一个深度阈值多特征提取模块来改进番茄病叶的特征提取。然后，提出了一种混合注意力机制，将交叉注意力模块与融合特征突出模块相结合，以协调图像的整体生成。最后，判别器采用马尔可夫判别器加强图像局部纹理的相似性判断。基于开放数据集PlantVillage，MMDGAN生成的健康番茄叶片图像、叶片霉菌、叶片卷曲和蜘蛛螨的Frechet Inception Distance（FID）得分分别为159.3010、164.4744、230.3825和254.9866。此后，使用迁移学习对合成图像和真实图像进行 B-ARNet 模型的训练，以对番茄疾病的四类进行分类。所提出的方法达到了 97.12% 的准确率，F1 值为 97.78%。所提出的方法显示了其优于现有方法的优势。B-ARNet 模型使用迁移学习对合成图像和真实图像进行训练，以对番茄疾病的四类进行分类。所提出的方法达到了 97.12% 的准确率，F1 值为 97.78%。所提出的方法显示了其优于现有方法的优势。B-ARNet 模型使用迁移学习对合成图像和真实图像进行训练，以对番茄疾病的四类进行分类。所提出的方法达到了 97.12% 的准确率，F1 值为 97.78%。所提出的方法显示了其优于现有方法的优势。