AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Application
On the map: Retrieval-augmented generation → Time series and agricultural RAG
Abstract (arXiv)
AgroLLM is an AI-powered chatbot designed to enhance knowledge-sharing and education in agriculture using Large Language Models (LLMs) and a Retrieval-Augmented Generation (RAG) framework. By using a comprehensive open-source agricultural database, AgroLLM provides accurate, contextually relevant responses while reducing incorrect information retrieval. The system utilizes the FAISS vector database for efficient similarity searches, ensuring rapid access to agricultural knowledge. A comparative study of three advanced models: Gemini 1.5 Flash, ChatGPT-4o Mini, and Mistral-7B-Instruct-v0.2 was conducted to evaluate performance across four key agricultural domains: Agriculture and Life Sciences, Agricultural Management, Agriculture and Forestry, and Agriculture Business. Key evaluation metrics included embedding quality, search efficiency, and response relevance. Results indicated that ChatGPT-4o Mini with RAG achieved the highest accuracy at 93%. Continuous feedback mechanisms enhance response quality, making AgroLLM a benchmark AI-driven educational tool for farmers, researchers, and professionals, promoting informed decision-making and improved agricultural practices.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Time series and agricultural RAG, one of 15 directions in Retrieval-augmented generation. Builds on 13 of its 44 references that are in the map. First posted on arXiv 28 Feb 2025.
Builds on
13 of 44 references.
References · 13
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- 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
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- 2023Embedding-based Retrieval with LLM for Effective Agriculture Information Extracting from Unstructured Datacited by 16
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Built on by
Citing papers per year: 2025: 3; 2026: 5.
8 papers.
- 2025AgriGPT-Omni: A Unified Speech-Vision-Text Framework for Multilingual Agricultural Intelligencecited by 4
- 2025Towards Large Reasoning Models for Agriculturecited by 3
- 2026Cost-Efficient Cross-Lingual Retrieval-Augmented Generation for Low-Resource Languages: A Case Study in Bengali Agricultural Advisorycited by 2
- 2025KrishokBondhu: A Retrieval-Augmented Voice-Based Agricultural Advisory Call Center for Bengali Farmerscited by 1
- 2026AgriLens: Semantic Retrieval in Agricultural Texts Using Topic Modeling and Language Modelscited by 0
- 2026Towards AI Evaluation in Domain-Specific RAG Systems: The AgriHubi Case Studycited by 0
- 2026AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoningcited by 0
- 2026AgriIR: A Scalable Framework for Domain-Specific Knowledge Retrievalcited by 0
- 2026AgriIR: A Scalable Framework for Domain-Specific Knowledge Retrieval17 Mar 2026
- 2026AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoning15 Mar 2026
- 2026Towards AI Evaluation in Domain-Specific RAG Systems: The AgriHubi Case Study2 Feb 2026
- 2026AgriLens: Semantic Retrieval in Agricultural Texts Using Topic Modeling and Language Models13 Jan 2026
- 2026Cost-Efficient Cross-Lingual Retrieval-Augmented Generation for Low-Resource Languages: A Case Study in Bengali Agricultural Advisory5 Jan 2026
- 2025AgriGPT-Omni: A Unified Speech-Vision-Text Framework for Multilingual Agricultural Intelligence11 Dec 2025
- 2025KrishokBondhu: A Retrieval-Augmented Voice-Based Agricultural Advisory Call Center for Bengali Farmers21 Oct 2025
- 2025Towards Large Reasoning Models for Agriculture25 May 2025
Similar, not cited
Close in wording, no citation either way
- 2025AgriGPT: a Large Language Model Ecosystem for Agriculture
- 2024ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources
- 2025Intent Aware Context Retrieval for Multi-Turn Agricultural Question Answering
- 2025AgMMU: A Comprehensive Agricultural Multimodal Understanding Benchmark
- 2024AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning
- 2024RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
- 2023GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models
- 2025Towards an AI-based knowledge assistant for goat farmers based on Retrieval-Augmented Generation