AI–Blockchain Hybrid Framework for Agricultural Insurance

Agricultural insurance is a critical risk mitigation mechanism for farmers facing uncertainties caused by climate change, weather variability, crop diseases, and yield fluctuations. Despite large-scale government-backed initiatives such as the Pradhan Mantri Fasal Bima Yojana (PMFBY), conventional agricultural insurance systems continue to suffer from delayed claim settlements, subjective damage assessment, lack of transparency, and high administrative costs. This paper proposes a comprehensive and plagiarism-safe AI–Blockchain hybrid framework to automate agricultural insurance operations. Artificial intelligence models analyze multi-source data collected from IoT sensors, satellite imagery, and weather services to perform crop health monitoring, yield prediction, and quantitative risk scoring. Blockchain technology is employed to store insurance policies, ensure data immutability, and execute smart contracts for automated claim settlement. AI-generated risk scores act as trusted oracle inputs to trigger on-chain claim execution. The proposed framework improves efficiency, transparency, and trust, making it suitable for large-scale deployment in government and private agricultural insurance schemes.Agricultural insurance is a critical risk mitigation mechanism for farmers facing uncertainties caused by climate change, weather variability, crop diseases, and yield fluctuations. Despite large-scale government-backed initiatives such as the Pradhan Mantri Fasal Bima Yojana (PMFBY), conventional agricultural insurance systems continue to suffer from delayed claim settlements, subjective damage assessment, lack of transparency, and high administrative costs. This paper proposes a comprehensive and plagiarism-safe AI–Blockchain hybrid framework to automate agricultural insurance operations. Artificial intelligence models analyze multi-source data collected from IoT sensors, satellite imagery, and weather services to perform crop health monitoring, yield prediction, and quantitative risk scoring.