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.
Authors
- Ferhat Sadak (ORCID: https://orcid.org/0000-0003-2391-4836)
- Manoj Karkee (ORCID: https://orcid.org/0000-0001-5337-4848)
- Maged Shoman (ORCID: https://orcid.org/0000-0002-4265-071X)
- Ranjan Sapkota (ORCID: https://orcid.org/0000-0002-5417-6744)
- Zhichao Meng
- John Shutske (ORCID: https://orcid.org/0000-0002-4253-8314)
- Rizwan Qureshi (ORCID: https://orcid.org/0000-0002-0039-982X)
- Achyut Paudel (ORCID: https://orcid.org/0000-0002-2490-8043)
- Fayaz Ali Dharejo (ORCID: https://orcid.org/0000-0001-7685-3913)
- Muhammad Usman Hadi (ORCID: https://orcid.org/0000-0002-3363-2886)
- Muhammad Sajjad (ORCID: https://orcid.org/0000-0002-1576-1342)
- Syed Zohaib Hassan (ORCID: https://orcid.org/0000-0002-2134-8108)
- Jiajia Li (ORCID: https://orcid.org/0000-0003-2894-6458)
Institutions
- 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)
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