Modernizing Agriculture with Real-Time Crop Insurance: Leveraging Blockchain, IoT, and Machine Learning
Numerous issues confronting modern agriculture are unpredictability in weather patterns, inefficiencies in the supply chain, and insufficient risk management systems. This study uses Blockchain and Internet of Things (IoT) technology to integrate responsive crop insurance mechanisms, offering a revolutionary approach to crop production. By placing IoT sensors in farmers' fields, real-time data on vital factors affecting crop development may be collected. This data serves as the basis for a framework for dynamic crop insurance. These Internet of Things (IoT) sensors track temperature, humidity, soil moisture content, and other environmental variables, giving farmers useful information for risk management and proactive decision-making.When there are natural disasters or unfavorable weather patterns, predetermined parameters gathered by IoT devices speed up the evaluation of insurance claims and reduce waiting times. Farmers are encouraged to be resilient due to this expedited procedure, which guarantees timely payment for crop losses. By integrating Blockchain technology, the insurance framework's transactions are guaranteed to be secure and honest, reducing the risk of fraud and boosting stakeholder trust. We are using Machine Learning Algorithms to predict the health of the plant, by detecting diseases caused, we leverage this by uploading crop photographs. This ground-breaking solution transforms the security and efficiency of agricultural production and insurance while resolving a major issue for farmers with an accuracy of near 97% This research helps agriculture's sustainable growth by equipping farmers with cutting-edge technologies and responsive insurance arrangements, promoting resilience and prosperity in rural communities.
Authors
- Dipmala Salunke (ORCID: https://orcid.org/0000-0002-2921-5342)
- Pallavi Tekade (ORCID: https://orcid.org/0000-0003-2770-5484)
- Sonali Rangdale (ORCID: https://orcid.org/0000-0003-0802-3365)
- Siddhi Sattur
- Sakshi Padwal
- Atharv Shinde
Institutions
- Savitribai Phule Pune University (IN)
Publication Details
- Published
- 2024-10-17
- DOI
- https://doi.org/10.1109/icbds61829.2024.10837140
- Citations
- 1
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.60