International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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“Leveraging Artificial Intelligence for Risk Assessment in Crop Insurance PMFBJ : A Study on Enhancing Agricultural Resilience”

Author(s) Mr. SHIVAKUMAR H N, Mr. H C Sudheendra Mouli, Ms. Nandini R V
Country India
Abstract The agricultural sector is increasingly vulnerable to a range of risks, including erratic weather patterns, pest infestations, and crop failures, which pose significant challenges to both farmers and insurers. Traditional methods of risk assessment in crop insurance often suffer from inefficiencies, delays, and inaccuracies due to limited data availability and manual processes. This research explores the transformative role of Artificial Intelligence (AI) in enhancing the precision and effectiveness of risk assessment within crop insurance schemes. By leveraging technologies such as machine learning, remote sensing, satellite imagery, and predictive analytics, AI has the potential to revolutionize how insurers evaluate agricultural risks, determine premiums, and expedite claims processing.
This study aims to assess the integration of AI-driven tools in crop insurance, evaluate their impact on agricultural resilience, and identify the barriers to widespread adoption. Through a combination of secondary data analysis and case studies of AI applications in crop insurance programs, the research investigates how AI can contribute to more equitable, timely, and data-driven insurance services. The findings are expected to provide valuable insights for policymakers, insurers, and agritech companies aiming to foster a more resilient and adaptive agricultural ecosystem. Ultimately, this study contributes to the growing body of knowledge on digital transformation in agriculture and underscores the critical role of AI in mitigating the impacts of climate variability and enhancing food security.
Keywords Artificial Intelligence, Crop Insurance, Risk Assessment, Agricultural Resilience
Field Computer Applications
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-19
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.46720
Short DOI https://doi.org/g9qxcs
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