Explainable AI-Based Farmer Advisory System for Agricultural Risk, Subsidy, Loan, and Market Decision Support

This research presents an Explainable Artificial Intelligence (XAI)-based framework designed to support intelligent farmer advisory services related to loans, subsidies, crop insurance, agricultural risk analysis, and market forecasting. The proposed system integrates machine learning models with explainable outputs to improve transparency and help farmers better understand recommendation decisions. The framework evaluates multiple agricultural factors, including weather conditions, soil quality, and market trends to calculate agricultural risk scores and generate personalized advisory support. The system also incorporates multilingual communication features using Gemini API support for Kannada, Hindi, and English to improve accessibility for rural users. A comparative literature survey and simulated analytical evaluation demonstrate that the proposed framework can enhance transparency, improve farmer trust in digital advisory systems, and support welfare-oriented agricultural decision-making. Future enhancements include real-time API integration, IoT-based soil monitoring, blockchain-enabled subsidy tracking, and cloud-based deployment infrastructure.