A Comprehensive Review of Diffusion Models in Smart Agriculture: Progress, Applications, and Challenges
Xing Hu, Haodong Chen, Qianqian Duan, Dawei Zhang
TL;DR
The paper surveys diffusion models as a powerful, stable alternative to GANs for smart agriculture, addressing data scarcity and long-tail problems in crop disease, pest detection, and remote sensing. It outlines core diffusion techniques (DDPM, DDIM, score-based models, NCSN, CDMs, and LDMs) and their applicability to agricultural tasks, including data augmentation, image generation, and multi-modal fusion. Through empirical comparisons, it demonstrates that diffusion-based synthetic data can improve classification, detection, and remote-sensing reconstruction while enabling more robust, domain-adaptive performance, albeit with high computational costs. The review also discusses challenges—computational efficiency, generalization, and data quality—and sketches future directions toward efficient, multimodal, and cross-domain diffusion pipelines with real-world impact on precision agriculture and sustainability.
Abstract
With the global population increasing and arable land resources becoming increasingly limited, smart and precision agriculture have emerged as essential directions for sustainable agricultural development. Artificial intelligence (AI), particularly deep learning models, has been widely adopted in applications such as crop monitoring, pest detection, and yield prediction. Among recent generative models, diffusion models have demonstrated considerable potential in agricultural image processing, data augmentation, and remote sensing analysis. Compared to traditional generative adversarial networks (GANs), diffusion models exhibit greater training stability and superior image generation quality, effectively addressing challenges such as limited annotated datasets and imbalanced sample distributions in agricultural scenarios. This paper reviews recent advancements in the application of diffusion models within agriculture, focusing on their roles in crop disease and pest detection, remote sensing image enhancement, crop growth prediction, and agricultural resource management. Diffusion models have been found useful in improving tasks like image generation, denoising, and data augmentation in agriculture, especially when environmental noise or variability is present. While their high computational requirements and limited generalizability across domains remain concerns, the approach is gradually proving effective in real-world applications such as precision crop monitoring. As research progresses, these models may help support sustainable agriculture and address emerging challenges in food systems.