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Leveraging large language models (LLMs) for GeoAI-enabled digital agro-advisory
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DOI：10.3389/frsen.2026.1839369.png)
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Abstract
En 中文
Digital agriculture has undergone a profound transformation driven by rapid advances in Earth observation; unmanned aerial vehicles and advanced data processing techniques. Despite this progress; climate-smart agriculture management faces a critical challenge: a farmer-centric agro-advisory system. Farmers and extension workers have limited access to GeoAI-based agro-advisory services; primarily due to limited data access and insufficient technical support. This study synthesises the potential of Large Language Models to address this challenge by translating complex GeoAI data into actionable and human-centric advice. The review examines gaps; recent advancements; and the model architectures required to adapt general-purpose LLMs for remote-sensing-based crop monitoring and management. We distinguish between experimentally validated capabilities of LLMs and the broader prospective applications proposed for agricultural remote sensing; noting that many current advances are primarily driven by multimodal foundation models; computer vision; and GeoAI systems rather than standalone LLMs. The study highlights the importance of advanced Retrieval-Augmented Generation and Supervised Fine-Tuning in agronomic science and mitigating the risk of misinterpretation. Further; we examine the emerging capabilities of multimodal approaches; which can seamlessly integrate visualisation and textual reasoning to support stakeholders in assessing crop health conditions and biophysical anomalies. The representative case studies; spanning multiple geographies and including voice-based advisory systems; demonstrate the shift from static advisory tools to dynamic; interactive recommendation systems. We highlight current challenges; such as the need for region-specific fine-tuning; data governance; and operation in low-connectivity environments; and advocate for a “human-in-the-loop” approach to decision-making. Through this; the LLMs will function as co-pilots assisting multiple stakeholders rather than as autonomous decision-makers vulnerable to biased output or hallucination problems. The review concludes by outlining a forward-looking research scope and closed-loop systems that iteratively learn from field outcomes.
Keywords:
human-in-the-loop
remote sensing
precision farming
large language models (LLMs)
digital agriculture
multimodal AI
agro-advisory
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3.7
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560
Citations:
993
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