@article{Wu2026, 
author = {Xingcai Wu and Qiaoling Wan and Ya Yu and Yujiao Dan and Hanying Xie and G.M.A.D. Sirishantha and Qi Wang and Gefei Hao and Yongjin Liu},
title = {LesionDiff: Synthetic data via lesion information transfer diffusion model facilitates plant disease diagnosis},
year = {2026},
journal = {The Crop Journal},
volume = {14},
number = {3},
pages = {1051-1063},
keywords = {Plant disease, Lesion synthesis, Diffusion model, Data enhancement},
url = {https://www.sciopen.com/article/10.1016/j.cj.2026.01.011},
doi = {10.1016/j.cj.2026.01.011},
abstract = {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%.}
}