Diffusion Models for Agricultural Imaging: A Systematic Review of Methods, Applications and Future Prospects

Abstract

Diffusion models are rapidly reshaping agricultural image analysis, offering high-fidelity synthetic data generation where real datasets are limited, imbalanced, or costly to collect. Traditional augmentation and GAN-based synthesis often struggle to preserve fine disease features and crop textures, leading to suboptimal model performance in real field conditions. This review consolidates the latest research on diffusion-based methods applied to plant disease diagnosis, fruit quality assessment, weed and pest monitoring, nematode identification, green-wall health evaluation, and UAV-based phenotyping. Reported literature demonstrates improved texture detail, lesion clarity, and better classification accuracy when diffusion-generated images supplement training datasets. Techniques such as latent diffusion and ControlNet enhance structure control, while text-guided models support domain transfer and unseen class synthesis. Despite promising outcomes, challenges remain concerning computational cost, real-world generalization across farms and seasons, and lack of standardized evaluation protocols. Future progress is expected through multimodal diffusion integrating hyperspectral and thermal inputs, efficient deployment on edge devices, and development of open benchmarks for comparative analysis. This review positions diffusion models as a leading generative approach for agricultural AI and outlines the research opportunities needed for practical adoption in large-scale farming environments.Diffusion Models; Synthetic Data Generation; Agricultural Imaging; Plant Disease Detection; Weed and Pest Monitoring; UAV Crop Phenotyping; Deep Learning in Agriculture.