  The past decade has witnessed the rapid development and adoption of ML & DL
methodologies in agricultural systems, showcased by great successes in
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, large pre-trained models, also known as FMs, have
demonstrated remarkable successes in language, vision, and decision-making
tasks across various domains. These models are trained on a large 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 AI. Thus, this study aims to
explore the potential of FMs in the field of smart agriculture. In particular,
conceptual tools and technical background are presented to help the
understanding of the problem space and uncover new research directions. To this
end, recent FMs in the general CS domain are reviewed, and the models are
categorized into four categories: language FMs, vision FMs, multimodal FMs, and
reinforcement learning FMs. Then, the steps of developing agriculture FMs
(AFMs) are outlined and potential applications in smart agriculture are
discussed. Moreover, challenges and risks associated with developing AFMs are
discussed, including model training, validation, and deployment. In summary,
the advancement of AI in agriculture is explored 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.
