Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{180030,
        author = {Nandan Kumar S and Chinmayi C S and Suman K and Murali Krishna N and Madhu B R and Vinod Kumar},
        title = {AGROMIND AI: EMPOWERING FARMERS WITH CONTEXT-AWARE, LLM - DRIVEN AGRICULTURAL INTELLIGENCE},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {183-188},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180030},
        abstract = {AgroMind AI is a voice-enabled, context
aware assistant that leverages Large Language Models 
(LLMs) and modular agents to deliver real-time 
localized agricultural support. Integrating tools for web 
search, document parsing, and media curation provides 
intelligent responses in regional languages, even offline. 
Built using Mixtral-8×7b, LangChain, and ChromaDB, 
the system addresses the key rural challenges of low 
connectivity, language diversity, and information 
overload. Tested across multiple use cases, AgroMind 
AI demonstrated high accuracy, rapid response times, 
and strong scalability, making it a practical solution for 
AI-driven farming assistance in India.},
        keywords = {AgroMind AI, Large Language Models  (LLMs), Voice Assistant, Multilingual AI, Context-Aware  Systems, Semantic Search, ChromaD, LangChain.},
        month = {May},
        }