A Comprehensive Review of Diffusion Models in Smart Agriculture: Progress, Applications, and Challenges

TL;DR

This review highlights the growing role of diffusion models in smart agriculture, emphasizing their advantages in image processing and data augmentation, while discussing current challenges and future potential for sustainable farming.

Contribution

It provides a comprehensive overview of recent advancements and applications of diffusion models in agriculture, comparing them to GANs and identifying key challenges.

Findings

Diffusion models outperform GANs in image quality and training stability.

They are effective in crop disease detection and remote sensing enhancement.

High computational cost remains a challenge.

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.