A Secured Triad of IoT, Machine Learning, and Blockchain for Crop Forecasting in Agriculture
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912357857361920 |
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| author | Sizan, Najmus Sakib Layek, Md. Abu Hasan, Khondokar Fida |
| author_facet | Sizan, Najmus Sakib Layek, Md. Abu Hasan, Khondokar Fida |
| contents | To improve crop forecasting and provide farmers with actionable data-driven insights, we propose a novel approach integrating IoT, machine learning, and blockchain technologies. Using IoT, real-time data from sensor networks continuously monitor environmental conditions and soil nutrient levels, significantly improving our understanding of crop growth dynamics. Our study demonstrates the exceptional accuracy of the Random Forest model, achieving a 99.45\% accuracy rate in predicting optimal crop types and yields, thereby offering precise crop projections and customized recommendations. To ensure the security and integrity of the sensor data used for these forecasts, we integrate the Ethereum blockchain, which provides a robust and secure platform. This ensures that the forecasted data remain tamper-proof and reliable. Stakeholders can access real-time and historical crop projections through an intuitive online interface, enhancing transparency and facilitating informed decision-making. By presenting multiple predicted crop scenarios, our system enables farmers to optimize production strategies effectively. This integrated approach promises significant advances in precision agriculture, making crop forecasting more accurate, secure, and user-friendly. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01196 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Secured Triad of IoT, Machine Learning, and Blockchain for Crop Forecasting in Agriculture Sizan, Najmus Sakib Layek, Md. Abu Hasan, Khondokar Fida Machine Learning To improve crop forecasting and provide farmers with actionable data-driven insights, we propose a novel approach integrating IoT, machine learning, and blockchain technologies. Using IoT, real-time data from sensor networks continuously monitor environmental conditions and soil nutrient levels, significantly improving our understanding of crop growth dynamics. Our study demonstrates the exceptional accuracy of the Random Forest model, achieving a 99.45\% accuracy rate in predicting optimal crop types and yields, thereby offering precise crop projections and customized recommendations. To ensure the security and integrity of the sensor data used for these forecasts, we integrate the Ethereum blockchain, which provides a robust and secure platform. This ensures that the forecasted data remain tamper-proof and reliable. Stakeholders can access real-time and historical crop projections through an intuitive online interface, enhancing transparency and facilitating informed decision-making. By presenting multiple predicted crop scenarios, our system enables farmers to optimize production strategies effectively. This integrated approach promises significant advances in precision agriculture, making crop forecasting more accurate, secure, and user-friendly. |
| title | A Secured Triad of IoT, Machine Learning, and Blockchain for Crop Forecasting in Agriculture |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.01196 |