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.Comment: 16 pages, 2 figure