Multi-Modal LLMs in Agriculture: A Comprehensive Review

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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Publication Details

Journal
IEEE Transactions on Automation Science and Engineering
Published
2025-01-01
DOI
https://doi.org/10.1109/tase.2025.3612154
Citations
11
Primary Topic
Advanced Machining and Optimization Techniques
Type
article
Field-Weighted Citation Impact
8.03
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article

Multi-Modal LLMs in Agriculture: A Comprehensive Review

Ferhat Sadak, Manoj Karkee, Maged Shoman, Ranjan Sapkota et al.
11 citations
IEEE Transactions on Automation Science and Engineering
Advanced Machining and Optimization Techniques
8.03
article

Multi-Modal LLMs in Agriculture: A Comprehensive Review

Ferhat Sadak, Manoj Karkee, Maged Shoman, Ranjan Sapkota, Zhichao Meng, John Shutske, Rizwan Qureshi, Achyut Paudel, Fayaz Ali Dharejo, Muhammad Usman Hadi, Muhammad Sajjad, Syed Zohaib Hassan, Jiajia Li
article en
11 citations

Abstract

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.

IEEE Transactions on Automation Science and EngineeringVol. 22
University of Central Florida (US), Zhejiang A & F University (CN), University of Wisconsin System (US), Zhejiang Sci-Tech University (CN), University of Wisconsin–Madison (US), University of Ulster (GB), Chinese University of Hong Kong (HK), Cornell University (US), Bartin University (TR), University of Würzburg (DE), Ulster Hospital (GB), Washington State University (US), University of Tennessee at Knoxville (US), Michigan State University (US), University of Hong Kong (HK)
Openalex Percentile: Top 2%
Advanced Machining and Optimization Techniques
8.03
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