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

• URL: http://arxiv.org/abs/2512.10114v1
• Date: Wed, 10 Dec 2025 22:06:41 GMT
• Title: AgriRegion: Region-Aware Retrieval for High-Fidelity Agricultural Advice
• Authors: Mesafint Fanuel, Mahmoud Nabil Mahmoud, Crystal Cook Marshal, Vishal Lakhotia, Biswanath Dari, Kaushik Roy, Shaohu Zhang,
• Abstract summary: AgriRegion is a Retrieval-Augmented Generation (RAG) framework designed specifically for high-fidelity, region-aware agricultural advisory.<n>By restricting the knowledge base to verified local agricultural extension services, AgriRegion ensures that the advice regarding planting schedules, pest control, and fertilization is locally accurate.
• Score: 5.67768730948654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• AgriDoctor: A Multimodal Intelligent Assistant for Agriculture [45.77373971125537]
  AgriDoctor is a modular and multimodal framework designed for intelligent crop disease diagnosis and agricultural knowledge interaction.<n>To facilitate effective training and evaluation, we construct AgriMM, a benchmark comprising 400000 annotated disease images, 831 expert-curated knowledge entries, and 300000 bilingual prompts for intent-driven tool selection.<n>Experiments demonstrate that AgriDoctor, trained on AgriMM, significantly outperforms state-of-the-art LVLMs on fine-grained agricultural tasks.
  arXiv  Detail & Related papers  (2025-09-21T11:51:57Z)
• AgriGPT: a Large Language Model Ecosystem for Agriculture [16.497060004913806]
  AgriGPT is a domain-specialized Large Language Models ecosystem for agriculture usage.<n>At its core, we design a scalable data engine that compiles credible data sources into Agri-342K, a high-quality, standardized question-answer dataset.<n>We employ Tri-RAG, a three-channel Retrieval-Augmented Generation framework combining dense retrieval, sparse retrieval, and multi-hop knowledge graph reasoning.
  arXiv  Detail & Related papers  (2025-08-12T04:51:08Z)
• Satellite-based Rabi rice paddy field mapping in India: a case study on Telangana state [0.0]
  This study developed a phenology-driven framework that adapts to local agro-ecological variations across 32 districts in India during the 2018-19 rice season.<n>Our district-specific calibration approach achieved 93.3% overall accuracy, an 8.0 percentage point improvement over conventional regional clustering methods.
  arXiv  Detail & Related papers  (2025-07-07T16:47:37Z)
• AgriCHN: A Comprehensive Cross-domain Resource for Chinese Agricultural Named Entity Recognition [30.51577375197722]
  We present AgriCHN, a comprehensive open-source Chinese resource designed to promote the accuracy of automated agricultural entity annotation.<n>The dataset has been meticulously curated from a wealth of agricultural articles, comprising a total of 4,040 sentences and encapsulating 15,799 agricultural entity mentions.<n>A benchmark task has also been constructed using several state-of-the-art neural NER models.
  arXiv  Detail & Related papers  (2025-06-21T04:21:11Z)
• AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.0 [0.5461938536945723]
  We employ emerging technologies such as the Internet of Things (IoT), machine learning (ML), and explainable artificial intelligence (XAI) to improve operational efficiency and productivity in the agricultural sector.<n>Specifically, we propose an edge computing-based explainable crop recommendation system, AgroXAI, which suggests suitable crops for a region based on weather and soil conditions.
  arXiv  Detail & Related papers  (2024-12-16T20:18:10Z)
• Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases [49.782064512495495]
  We construct the first multimodal instruction-following dataset in the agricultural domain.<n>This dataset covers over 221 types of pests and diseases with approximately 400,000 data entries.<n>We propose a knowledge-infused training method to develop Agri-LLaVA, an agricultural multimodal conversation system.
  arXiv  Detail & Related papers  (2024-12-03T04:34:23Z)
• AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models [4.12825661607328]
  AgriBench is the first benchmark designed to evaluate MultiModal Large Language Models (MM-LLMs) for agriculture applications.<n>We propose MM-LUCAS, a multimodal agriculture dataset that includes 1,784 landscape images, segmentation masks, depth maps, and detailed annotations.<n>This work presents a groundbreaking perspective in advancing agriculture MM-LLMs and is still in progress, offering valuable insights for future developments and innovations in specific expert knowledge-based MM-LLMs.
  arXiv  Detail & Related papers  (2024-11-30T12:59:03Z)
• Anticipatory Understanding of Resilient Agriculture to Climate [66.008020515555]
  We present a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. We focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population.
  arXiv  Detail & Related papers  (2024-11-07T22:29:05Z)
• Generating Diverse Agricultural Data for Vision-Based Farming Applications [74.79409721178489]
  This model is capable of simulating distinct growth stages of plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture.
  arXiv  Detail & Related papers  (2024-03-27T08:42:47Z)
• HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing [50.4506590177605]
  HarvestNet is a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023. We introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions.
  arXiv  Detail & Related papers  (2023-08-23T11:03:28Z)
• Domain Generalization for Crop Segmentation with Standardized Ensemble Knowledge Distillation [42.39035033967183]
  Service robots need a real-time perception system that understands their surroundings and identifies their targets in the wild. Existing methods, however, often fall short in generalizing to new crops and environmental conditions. We propose a novel approach to enhance domain generalization using knowledge distillation.
  arXiv  Detail & Related papers  (2023-04-03T14:28:29Z)
• Jalisco's multiclass land cover analysis and classification using a novel lightweight convnet with real-world multispectral and relief data [51.715517570634994]
  We present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis. In this work, we combine three real-world open data sources to obtain 13 channels. Our embedded analysis anticipates the limited performance in some classes and gives us the opportunity to group the most similar.
  arXiv  Detail & Related papers  (2022-01-26T14:58:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.