AI-Based Farmer Query & Advisory System

  This paper presented an AI-Based Farmer Query and Advisory System designed to provide accurate, context-aware, and personalized agricultural guidance. By integrating AI-driven query analysis, behavioral modeling, and contextual interpretation, the proposed system moves beyond traditional static advisory platforms, enabling real-time and practical recommendations for farmers. The experimental evaluation demonstrated that the AI-driven system achieves higher advisory accuracy and relevance compared to conventional rule-based or keyword-matching systems. Contextual interpretation based on crop type, season, location, and historical query patterns proved effective in delivering personalized guidance, improving farmer satisfaction and engagement. Response latency remained low, ensuring real-time interaction suitable for mobile and web interfaces.   Furthermore, the integration of AI-driven decision-making with a dynamic agricultural knowledge base enhances advisory automation while maintaining flexibility and adaptability. By continuously learning from new queries and environmental conditions, the system strengthens its capability to provide actionable guidance for diverse farming scenarios, including rare crops and emerging pest or disease patterns.   Overall, the findings confirm that AI-driven farmer advisory systems represent a scalable, practical, and effective approach for delivering intelligent agricultural support. The proposed framework bridges the gap between automated query understanding and actionable guidance, offering a future-ready solution to enhance farming productivity, sustainability, and decision-making.

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