# AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Application

arXiv: https://arxiv.org/abs/2503.04788 · HTML: https://mlsanity.com/paper/2503.04788/

## Key facts

- Cited by 8 papers in the map (data through 14 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.

## Placed in

- Retrieval-augmented generation / [Time series and agricultural RAG](https://mlsanity.com/rag/time-series-and-agricultural-rag/)

## Builds on (most cited first, 13 of 13)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era](https://mlsanity.com/paper/2304.06488/) (2023)
- [ChatGPT: Applications, Opportunities, and Threats](https://mlsanity.com/paper/2304.09103/) (2023)
- [Embedding-based Retrieval with LLM for Effective Agriculture Information Extracting from Unstructured Data](https://mlsanity.com/paper/2308.03107/) (2023)
- [Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition](https://mlsanity.com/paper/2401.12599/) (2024)
- [ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic Agricultural Text Classification](https://mlsanity.com/paper/2305.15024/) (2023)
- [Platform-Independent and Curriculum-Oriented Intelligent Assistant for Higher Education](https://mlsanity.com/paper/2302.09294/) (2023)
- [VectorSearch: Enhancing Document Retrieval with Semantic Embeddings and Optimized Search](https://mlsanity.com/paper/2409.17383/) (2024)
- [Chatbots and ChatGPT: A Bibliometric Analysis and Systematic Review of Publications in Web of Science and Scopus Databases](https://mlsanity.com/paper/2304.05436/) (2023)

## Built on by (most cited first, 8 of 8)

- [AgriGPT-Omni: A Unified Speech-Vision-Text Framework for Multilingual Agricultural Intelligence](https://mlsanity.com/paper/2512.10624/) (2025)
- [Towards Large Reasoning Models for Agriculture](https://mlsanity.com/paper/2505.19259/) (2025)
- [Cost-Efficient Cross-Lingual Retrieval-Augmented Generation for Low-Resource Languages: A Case Study in Bengali Agricultural Advisory](https://mlsanity.com/paper/2601.02065/) (2026)
- [KrishokBondhu: A Retrieval-Augmented Voice-Based Agricultural Advisory Call Center for Bengali Farmers](https://mlsanity.com/paper/2510.18355/) (2025)
- [AgriLens: Semantic Retrieval in Agricultural Texts Using Topic Modeling and Language Models](https://mlsanity.com/paper/2601.08283/) (2026)
- [Towards AI Evaluation in Domain-Specific RAG Systems: The AgriHubi Case Study](https://mlsanity.com/paper/2602.02208/) (2026)
- [AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoning](https://mlsanity.com/paper/2603.14342/) (2026)
- [AgriIR: A Scalable Framework for Domain-Specific Knowledge Retrieval](https://mlsanity.com/paper/2604.16353/) (2026)

## 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.