Intent Aware Context Retrieval for Multi-Turn Agricultural Question Answering

• URL: http://arxiv.org/abs/2508.03719v1
• Date: Mon, 28 Jul 2025 09:00:44 GMT
• Title: Intent Aware Context Retrieval for Multi-Turn Agricultural Question Answering
• Authors: Abhay Vijayvargia, Ajay Nagpal, Kundeshwar Pundalik, Atharva Savarkar, Smita Gautam, Pankaj Singh, Rohit Saluja, Ganesh Ramakrishnan,
• Abstract summary: Indian farmers often lack timely, accessible, and language-friendly agricultural advice, especially in rural areas with low literacy.<n>This paper presents a novel AI-powered agricultural chatbots, Krishi Sathi, designed to support Indian farmers by providing personalized, easy-to-understand answers to their queries through both text and speech.<n>Krishi Sathi follows a structured, multi-turn conversation flow to gradually collect the necessary details from the farmer, ensuring the query is fully understood before generating a response.
• Score: 17.122839125789557
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Indian farmers often lack timely, accessible, and language-friendly agricultural advice, especially in rural areas with low literacy. To address this gap in accessibility, this paper presents a novel AI-powered agricultural chatbot, Krishi Sathi, designed to support Indian farmers by providing personalized, easy-to-understand answers to their queries through both text and speech. The system's intelligence stems from an IFT model, subsequently refined through fine-tuning on Indian agricultural knowledge across three curated datasets. Unlike traditional chatbots that respond to one-off questions, Krishi Sathi follows a structured, multi-turn conversation flow to gradually collect the necessary details from the farmer, ensuring the query is fully understood before generating a response. Once the intent and context are extracted, the system performs Retrieval-Augmented Generation (RAG) by first fetching information from a curated agricultural database and then generating a tailored response using the IFT model. The chatbot supports both English and Hindi languages, with speech input and output features (via ASR and TTS) to make it accessible for users with low literacy or limited digital skills. This work demonstrates how combining intent-driven dialogue flows, instruction-tuned models, and retrieval-based generation can improve the quality and accessibility of digital agricultural support in India. This approach yielded strong results, with the system achieving a query response accuracy of 97.53%, 91.35% contextual relevance and personalization, and a query completion rate of 97.53%. The average response time remained under 6 seconds, ensuring timely support for users across both English and Hindi interactions.

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