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. Empirical studies show that diffusion models significantly enhance the performance of downstream models by improving accuracy, robustness, and generalization in tasks involving image synthesis, augmentation, and denoising under complex environmental conditions. Despite ongoing challenges in computational efficiency and domain generalization, diffusion models are expected to play an increasingly important role in the future of intelligent agriculture. As the technology continues to evolve, it holds substantial promise for addressing pressing global issues in food security and environmental sustainability.

翻译：随着全球人口增长和耕地资源日益受限，智慧农业与精准农业已成为农业可持续发展的关键方向。人工智能（AI），尤其是深度学习模型，已广泛应用于作物监测、病虫害检测和产量预测等领域。在近期兴起的生成模型中，扩散模型在农业图像处理、数据增强和遥感分析方面展现出巨大潜力。相较于传统的生成对抗网络（GAN），扩散模型具有更强的训练稳定性和更优的图像生成质量，能有效应对农业场景中标注数据有限、样本分布不平衡等挑战。本文综述了扩散模型在农业领域应用的最新进展，重点探讨其在作物病虫害检测、遥感图像增强、作物生长预测及农业资源管理中的作用。实证研究表明，扩散模型通过提升复杂环境条件下图像合成、增强与去噪任务的准确性、鲁棒性和泛化能力，显著增强了下游模型的性能。尽管在计算效率和领域泛化方面仍面临挑战，扩散模型有望在未来智慧农业中发挥日益重要的作用。随着技术的持续发展，该模型在应对全球粮食安全和环境可持续性等紧迫问题上具有广阔前景。