SYSTEMATIC REVIEW article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1579355
This article is part of the Research TopicAdvanced Methods, Equipment and Platforms in Precision Field Crops Protection, Volume IIView all 16 articles
Harnessing Large Vision and Language Models in Agriculture: A Review
Provisionally accepted
- 1Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China
- 2Key Laboratory of Integrated Circuits and Microsystems (Guangxi Normal University), Education Department of Guangxi Zhuang Autonomous Region, Guilin, China
- 3Department of Computer Science, University of Aberdeen, Aberdeen, United Kingdom
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Large models can play important roles in many domains. Agriculture is another key factor affecting the lives of people around the world. It provides food, fabric, biofuel, and other essential resources for humanity. However, facing many challenges such as pests and diseases, soil degradation, global warming, and food security, how to steadily increase the yield in the agricultural sector is a problem that humans still need to solve. Large models can help farmers improve production efficiency and harvest by detecting a series of agricultural production tasks such as pests and diseases, soil quality, and seed quality. It can also help farmers make wise decisions through a variety of information, such as images, text, etc. Herein, we delve into the potential applications of large models in agriculture, from large language model (LLM) to large vision model (LVM). After gaining a deeper understanding of multimodal large language models (MLLM), it can be recognized that problems such as agricultural image processing, agricultural question answering systems, and agricultural machine automation can all be solved by large models. Large models have great potential in the field of agriculture. We outline the current applications of agricultural large models, and aims to emphasize the importance of large models in the domain of agriculture. In the end, we envisage a future in which famers use large models to accomplish many tasks in agriculture, which can greatly improve agricultural production efficiency and yield.
Keywords: large model1, Agriculture2, natural language processing3, computer vision4, multimodal model5
Received: 19 Feb 2025; Accepted: 28 Jul 2025.
Copyright: © 2025 Zhu, Qin, Su, Lin, Li and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Hongyan Zhu, Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China
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