TY - JOUR
T1 - Multi-Modal LLMs in Agriculture: A Comprehensive Review
AU - Sapkota, Ranjan
AU - Qureshi, Rizwan
AU - Hadi, Muhammad Usman
AU - Hassan, Syed Zohaib
AU - Sadak, Ferhat
AU - Shoman, Maged
AU - Sajjad, Muhammad
AU - Dharejo, Fayaz Ali
AU - Paudel, Achyut
AU - Li, Jiajia
AU - Meng, Zhichao
AU - Shutske, John
AU - Karkee, Manoj
N1 - Publisher Copyright:
© 2004-2012 IEEE.
PY - 2025/9/19
Y1 - 2025/9/19
N2 - 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.
AB - 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.
KW - Large Language Models (MM-LLMs)
KW - Generative Artificial Intelligence
KW - multi-modal MM-LLMs
KW - ChatGPT
KW - Language Processing
KW - Deep Learning
KW - Machine Learning
KW - Computer Vision
KW - Precision Agriculture
KW - Language Models
KW - Transformers
KW - Vision-Language Models
KW - Agricultural Data Analysis
UR - https://pure.ulster.ac.uk/en/publications/81672aa0-2701-4a36-9aae-9dc6551ec046
UR - https://www.scopus.com/pages/publications/105017077359
U2 - 10.1109/tase.2025.3612154
DO - 10.1109/tase.2025.3612154
M3 - Article
SN - 1545-5955
VL - 22
SP - 22510
EP - 22540
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
ER -
SDG 2 Zero Hunger
SDG 7 Affordable and Clean Energy
SDG 15 Life on Land