综述200+篇论文，系统盘点AI在农业三大领域的深度学习应用。
AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock
- 梳理视觉变换器、图文基础模型等主流AI技术在农业中的应用
- 覆盖作物病害、畜牧健康、水产监测等关键任务，总结性能指标
- 指出多模态融合、边缘部署与跨场景适应是未来重点方向
作物、渔业和畜牧业构成全球粮食生产的基石，对持续增长的人口供给至关重要。然而，这些领域面临气候变化、资源限制及可持续管理等严峻挑战。解决这些问题亟需高效、精准且可扩展的技术方案，凸显人工智能（AI）的重要性。本综述系统性地回顾了超过200篇研究工作，涵盖传统机器学习方法、先进深度学习技术（如视觉变换器）以及近期的视觉-语言基础模型（如CLIP），聚焦作物病害检测、畜禽健康管理、水生物种监测等多样化任务。此外，本文还探讨了主要实施挑战，包括数据异质性、数据集选择、评估指标及地理分布差异。最后，讨论了潜在的开放研究方向，强调多模态数据融合、高效边缘设备部署以及适用于多样化农耕环境的领域自适应AI模型的重要性。该领域快速发展，相关进展可通过项目页面持续追踪：https://github.com/umair1221/AI-in-Agriculture。
原文摘要 · Abstract (English)
Crops, fisheries and livestock form the backbone of global food production, essential to feed the ever-growing global population. However, these sectors face considerable challenges, including climate variability, resource limitations, and the need for sustainable management. Addressing these issues requires efficient, accurate, and scalable technological solutions, highlighting the importance of artificial intelligence (AI). This survey presents a systematic and thorough review of more than 200 research works covering conventional machine learning approaches, advanced deep learning techniques (e.g., vision transformers), and recent vision-language foundation models (e.g., CLIP) in the agriculture domain, focusing on diverse tasks such as crop disease detection, livestock health management, and aquatic species monitoring. We further cover major implementation challenges such as data variability and experimental aspects: datasets, performance evaluation metrics, and geographical focus. We finish the survey by discussing potential open research directions emphasizing the need for multimodal data integration, efficient edge-device deployment, and domain-adaptable AI models for diverse farming environments. Rapid growth of evolving developments in this field can be actively tracked on our project page: https://github.com/umair1221/AI-in-Agriculture
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