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Multi-Modal LLMs in Agriculture: A Comprehensive Review

农业 立场文件 计算机科学 数据科学 风险分析(工程) 管理科学 质量(理念) 农业生产力 透视图(图形) 钥匙(锁) 多样性(控制论) 水准点(测量) 业务 数据质量 职位(财务) 信息系统 环境规划 知识管理 桥(图论) 信息技术 标杆管理 过程管理 最终用户
作者
Ranjan Sapkota,Rizwan Qureshi,Muhammad Usman Hadi,Syed Zohaib Hassan,Ferhat Sadak,Maged Shoman,Muhammad Sajjad,Fayaz Ali Dharejo,Achyut Paudel,Jiajia Li,Zhichao Meng,John Shutske,Manoj Karkee
出处
期刊:IEEE Transactions on Automation Science and Engineering [Institute of Electrical and Electronics Engineers]
卷期号:22: 22510-22540 被引量:16
标识
DOI:10.1109/tase.2025.3612154
摘要

Given the rapid emergence and applications of Multi-Modal Large Language Models (MM-LLMs) across various scientific fields, insights regarding their applicability in agriculture are still only partially explored. This paper conducts an in-depth review of MM-LLMs in agriculture, focusing on understanding how MM-LLMs can be developed and implemented to optimize agricultural processes, increase efficiency, and reduce costs. Recent studies have explored the capabilities of MM-LLMs in agricultural information processing and decision-making. Despite these advancements, significant gaps persist, particularly in addressing domain-specific challenges such as variable data quality and availability, integration with existing agricultural systems, and the creation of robust training datasets that accurately represent complex agricultural environments. Moreover, a comprehensive understanding of the capabilities, challenges, and limitations of MM-LLMs in agricultural information processing and application is still missing. Exploring these areas is crucial to providing the community with a broader perspective and a clearer understanding of MM-LLMs’ applications, establishing a benchmark for the current state and emerging trends in this field. To bridge this gap, this survey reviews the progress of MM-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 MM-LLMs in agriculture. The findings indicate that multi-modal MM-LLMs not only simplify complex agricultural challenges but also significantly enhance decision-making and improve the efficiency of agricultural image processing. These advancements position MM-LLMs as an essential tool for the future of farming. For continued research and understanding, an organized and regularly updated list of papers on MM-LLMs is available at https://github.com/JiajiaLi04/Multi-Modal-LLMs-in-Agriculture.
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