Tomato Disease Image Synthesis Model Base on Diffusion and Attention Mechanism

It is difficult to collect disease pictures of the plants due to the seasonal and regional features of diseases which can lead to poor effect of recognition models. Moreover, conventional enhancement techniques like image rotation and scaling often fail to produce images that accurately reflect real disease characteristics. Taking tomato late blight disease as an example, its typical pathological features demonstrate a complex textural structure and spatial distribution pattern. This study proposes a diffusion model with an attention mechanism. By building two models, basic and enhanced, this study explores the effect of class embedding, attention mechanism and pathology feature simulation on the quality of generation. The results show that the pictures generated by the enhanced model are significantly better than the basic model in terms of rationalization of pathological features. The FID and KID values of the healthy and late blight categories are 74, 68, 0.21, 0.18 respectively, showing that the synthetic data were able to simulate the visual characteristics of real diseases to some degree. This study provides a low-cost data augmentation scheme for disease recognition model training under small sample conditions, which helps to improve the robustness of classification models.