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

TL;DR
AgriRegion is a specialized retrieval-augmented framework that enhances agricultural advice accuracy by incorporating geospatial data and local knowledge, reducing hallucinations and increasing trustworthiness in region-specific contexts.
Contribution
It introduces a novel geospatial-aware retrieval method and a benchmark dataset for region-specific agricultural advice, improving factual accuracy over general LLMs.
Findings
Reduces hallucinations by 10-20% compared to state-of-the-art models.
Improves trust scores in region-specific agricultural advice.
Demonstrates effectiveness across 12 agricultural subfields.
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…
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Taxonomy
TopicsSmart Agriculture and AI · Topic Modeling · Multimodal Machine Learning Applications
