LesionDiff: Synthetic data via lesion information transfer diffusion model facilitates plant disease diagnosis

Training software models for crop disease diagnosis requires large image datasets to achieve high accuracy. We describe a lesion information transfer diffusion model, LesionDiff, for generating image data that augments a real-world disease lesion image dataset. An information preprocessing module identifies lesion areas on leaves, an enhancement module captures diverse visual and semantic lesion features, and a generation module fills missing regions in masked disease images by synthesizing lesion phenotypes. This augmentation increased the average diagnostic accuracy of a test dataset by more than 3%.