With the global population growing and arable land resources becoming increasingly scarce,smart agriculture and precision agriculture have emerged as key directions for the future ofagricultural development.Artificial intelligence (AI) technologies, particularly deep learning models, have found widespread applications in areas such as crop monitoring and pest detection. As an emerging generative model, diffusion models have shown significant promise in tasks like agricultural image processing, data augmentation, and remote sensing. Compared to traditional generative adversarial networks (GANs), diffusion models offer superior training stability and generation quality, effectively addressing challenges such as limited agricultural data and imbalanced image samples. This paper reviews the latest advancements in the application of diffusion models in agriculture, focusing on their potential in crop pest and disease detection, remote sensing image enhancement, crop growth prediction, and agricultural resource management. Experimental results demonstrate that diffusion models significantly improve model accuracy and robustness in data augmentation, image generation, and denoising, especially in complex environments. Despite challenges related to computational efficiency and generalization capabilities, diffusion models are expected to play an increasingly important role in smart and precision agriculture as technology advances, providing substantial support for the sustainable development of global agriculture.

翻译：随着全球人口增长和耕地资源日益稀缺，智慧农业与精准农业已成为未来农业发展的关键方向。人工智能技术，特别是深度学习模型，已在作物监测与病虫害检测等领域得到广泛应用。作为新兴的生成模型，扩散模型在农业图像处理、数据增强和遥感等任务中展现出巨大潜力。相较于传统的生成对抗网络，扩散模型具有更优的训练稳定性和生成质量，能有效应对农业数据有限和图像样本不均衡等挑战。本文综述了扩散模型在农业领域应用的最新进展，重点探讨了其在作物病虫害检测、遥感图像增强、作物生长预测和农业资源管理等方面的潜力。实验结果表明，扩散模型在数据增强、图像生成和去噪等任务中显著提升了模型的准确性与鲁棒性，尤其在复杂环境下表现突出。尽管在计算效率和泛化能力方面仍面临挑战，但随着技术进步，扩散模型有望在智慧农业与精准农业中发挥日益重要的作用，为全球农业的可持续发展提供有力支撑。