The past decade has witnessed the rapid development of ML and DL
methodologies in agricultural systems, showcased by great successes in variety
of agricultural applications. However, these conventional ML/DL models have
certain limitations: They heavily rely on large, costly-to-acquire labeled
datasets for training, require specialized expertise for development and
maintenance, and are mostly tailored for specific tasks, thus lacking
generalizability. Recently, foundation models have demonstrated remarkable
successes in language and vision tasks across various domains. These models are
trained on a vast amount of data from multiple domains and modalities. Once
trained, they can accomplish versatile tasks with just minor fine-tuning and
minimal task-specific labeled data. Despite their proven effectiveness and huge
potential, there has been little exploration of applying FMs to agriculture
fields. Therefore, this study aims to explore the potential of FMs in the field
of smart agriculture. In particular, we present conceptual tools and technical
background to facilitate the understanding of the problem space and uncover new
research directions in this field. To this end, we first review recent FMs in
the general computer science domain and categorize them into four categories:
language FMs, vision FMs, multimodal FMs, and reinforcement learning FMs.
Subsequently, we outline the process of developing agriculture FMs and discuss
their potential applications in smart agriculture. We also discuss the unique
challenges associated with developing AFMs, including model training,
validation, and deployment. Through this study, we contribute to the
advancement of AI in agriculture by introducing AFMs as a promising paradigm
that can significantly mitigate the reliance on extensive labeled datasets and
enhance the efficiency, effectiveness, and generalization of agricultural AI
systems.

本研究旨在探索基于基础模型（foundation models）在智能农业领域的潜力，通过回顾计算机科学领域的最新研究，并将其归类为语言基础模型、视觉基础模型、多模态基础模型和强化学习基础模型，本文提出了农业基础模型的发展过程和潜在应用及其在模型训练、验证和部署过程中可能面临的挑战，旨在通过引入农业基础模型这一有前景的范式，显著减少对大规模标记数据集的依赖，同时提高农业人工智能系统的效率、效果和泛化能力。