AgriUFM: Unconditional-Flow-Matching-Based Generative Model for Creating Image–Mask Pairs of Agricultural Pests and Disease

Abstract

Pests and diseases are key biological stress factors affecting crop yield and quality. Semantic segmentation enables pixel-level localization and severity characterization, but its performance and generalization are constrained by the high cost of high-quality pixel-level annotations, limited labeled samples, and class imbalance in agricultural datasets. We propose AgriUFM, an unconditional flow-matching framework for joint image–mask generation in agricultural pest and disease scenarios. By learning a unified continuous probability flow over the joint distribution, the framework is designed to promote structural co-evolution and spatial consistency between generated RGB images and masks. Across four evaluated datasets, AgriUFM achieved lower FID and rFID than the evaluated GAN- and diffusion-based comparators, whereas IS performance was dataset-dependent. Within the evaluated ablation configurations, uniform time sampling with 25 sampling steps and the midpoint ODE solver yielded the most favourable observed quality–efficiency trade-off. Under the held-out test protocol, the joint UFM strategy achieved higher image–mask correspondence than the M2I and I2M conditional variants. In the evaluated downstream settings, AgriUFM-generated augmentation improved MIoU and PA for U-Net and TransUNet. These results indicate that joint distribution modelling is a promising approach for structurally coherent generative augmentation in the agricultural imaging tasks studied.