通过将生物物理参数与SAR和光学遥感数据集成,通过使用机器学习模型来预测大米产量
Sonam Sah1,2, Dipanwita Haldar3, R N Singh2
1G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India.
Scientific reports
|September 17, 2024
概括
准确的产预测对于粮食安全至关重要. 使用遥感和生物物理数据的机器学习模型提供可靠的早期估计,随着收获的接近而改善.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 全球人口增长和气候变化需要加强粮食安全措施.
- 米是数十亿人的主食,准确的产量预测对于全球粮食安全至关重要.
研究的目的:
- 使用机器学习模型预测移植 (DAT) 后45,60和90天的水产量.
- 评估将光学和合成孔径雷达 (SAR) 数据与作物生物物理参数相结合的有效性,以预测产量.
- 评估各种机器学习模型的性能,以预测早期的水产量.
主要方法:
- 该研究利用光学和SAR数据与作物生物物理参数相结合.
- 使用了机器学习模型,包括极端梯度增强 (XGB),神经网络 (NNET),立方体,支持向量回归和随机森林.
- 在印度Uttarakhand的两个大米季节,收益率预测为45,60和90 DAT.
主要成果:
- 机器学习模型展示了相对准确的早期大米产量估计.
- 对于夏季大米,XGB在所有预测阶段 (45,60,90 DAT) 始终表现最佳.
- 对于Kharif大米,XGB,NNET和Cubist分别是45,60和90 DAT的顶级型号. 预测准确度在收获接近时有所提高,90 DAT为两季度的最佳结果.
结论:
- 遥感数据和生物物理参数与机器学习模型的整合显著提高了早期大米产量预测.
- 这种方法支持农民,政策制定者和研究人员的知情决策,从而加强粮食安全规划和资源管理.
- 该研究强调了先进分析技术在优化农业实践和确保稳定的粮食供应方面的潜力.
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