用物联网+机器学习+区块链，让农田数据更准更安全。
A Secured Triad of IoT, Machine Learning, and Blockchain for Crop Forecasting in Agriculture
- 三技术融合：传感器实时采数，随机森林预测作物
- 模型准确率达99.45%，可生成多场景种植建议
- 以太坊上存证防篡改，农户能查实时历史数据
为提升作物预测精度并为农民提供可操作的数据驱动洞察，本文提出一种整合物联网（IoT）、机器学习与区块链的技术方案。通过物联网传感器网络实时监测环境条件和土壤养分水平，显著提升对作物生长动态的理解。研究证明，随机森林模型在预测最优作物类型与产量方面达到99.45%的准确率，实现精准作物预测与个性化推荐。为保障传感器数据的安全与完整性，系统集成以太坊区块链，确保预测数据不可篡改、可信可靠。利益相关方可通过直观在线界面访问实时与历史作物预测，提升透明度，支持科学决策。系统提供多种预测情景，帮助农民有效优化生产策略。该集成方法推动精准农业发展，使作物预测更准确、更安全、更易用。
原文摘要 · Abstract (English)
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.
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