Multi-Modal LLMs in Agriculture: A Comprehensive Review

Abstract

Given the rapid emergence and applications of Large Language Models (LLMs) across various scientific fields, insights regarding their applicability in agriculture are still only partially explored. This paper conducts an in-depth review of LLMs in agriculture, focusing on understanding how LLMs can be developed and implemented to optimize agricultural processes, increase efficiency, and reduce costs. Recent studies have explored the capabilities of LLMs in agricultural information processing and decision-making. Nevertheless, a comprehensive understanding of the capabilities, challenges, limitations, and future directions of LLMs in agricultural information processing and application remains in its early stages. Such exploration is essential to provide the community with a broader perspective and clearer understanding of LLMs' applications, serving as a baseline for the current state and trends in the subject matter. To bridge this gap, this survey reviews the progress of LLMs and their utilization in agriculture, with an additional focus on 11 key research questions (RQs), where 4 RQs are general and 7 RQs are agriculture focused. By addressing these RQs, this review outlines the current opportunities and challenges, limitations, and future roadmap for LLMs in agriculture. The findings indicate that multi-modal LLMs not only simplify complex agricultural challenges but also significantly enhance decision-making and improve the efficiency of agricultural image processing. These advancements position LLMs as an essential tool for the future of farming. For continued research and understanding, an organized and regularly updated list of papers on LLMs is available at https://github.com/JiajiaLi04/ Multi-Modal-LLMs-in-Agriculture.