AgriRegion: Region-Aware Retrieval for High-Fidelity Agricultural Advice

Mesafint Fanuel; Mahmoud Nabil Mahmoud; Crystal Cook Marshal; Vishal Lakhotia; Biswanath Dari; Kaushik Roy; Shaohu Zhang

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arXiv · 2512.10114

AgriRegion: Region-Aware Retrieval for High-Fidelity Agricultural Advice

Mesafint Fanuel·Mahmoud Nabil Mahmoud·Crystal Cook Marshal·Vishal Lakhotia·Biswanath Dari·Kaushik Roy·Shaohu Zhang

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Abstract

Large Language Models (LLMs) have demonstrated significant potential in democratizing access to information. However, in the domain of agriculture, general-purpose models frequently suffer from contextual hallucination, which provides non-factual advice or answers are scientifically sound in one region but disastrous in another due to variations in soil, climate, and local regulations. We introduce AgriRegion, a Retrieval-Augmented Generation (RAG) framework designed specifically for high-fidelity, region-aware agricultural advisory. Unlike standard RAG approaches that rely solely on semantic similarity, AgriRegion incorporates a geospatial metadata injection layer and a region-prioritized re-ranking mechanism. By restricting the knowledge base to verified local agricultural extension services and enforcing geo-spatial constraints during retrieval, AgriRegion ensures that the advice regarding planting schedules, pest control, and fertilization is locally accurate. We create a novel benchmark dataset, AgriRegion-Eval, which comprises 160 domain-specific questions across 12 agricultural subfields. Experiments demonstrate that AgriRegion reduces hallucinations by 10-20% compared to state-of-the-art LLMs systems and significantly improves trust scores according to a comprehensive evaluation.

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Mesafint Fanuel, Mahmoud Nabil Mahmoud, Crystal Cook Marshal, Vishal Lakhotia, Biswanath Dari, Kaushik Roy, Shaohu Zhang. 2025-12-10. AgriRegion: Region-Aware Retrieval for High-Fidelity Agricultural Advice. https://arxiv.org/abs/2512.10114

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