使用手持式NIR光谱仪构建近红外 (NIR) 土壤光谱数据集和预测机器学习模型
Colleen Partida1, Jose Lucas Safanelli1, Sadia Mannan Mitu2
1Woodwell Climate Research Center, 149 Woods Hole Rd., Falmouth, MA, 02540, United States.
Data in brief
|January 15, 2025
概括
这项研究提供了来自2,106个不同矿物土壤样本的近红外光谱数据集. 机器学习模型被开发用于预测土壤的特性,如土壤有机碳 (SOC) 和pH,帮助土壤监测.
科学领域:
- 土壤科学 土壤科学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 从各种矿物土壤中编制了近红外 (NIR) 光谱的大数据集.
- 土壤样本来自美国和非洲,代表了各种土壤类型.
- 使用手持式光谱仪在干燥和选的土壤样本 (<2毫米) 上收集光谱.
研究的目的:
- 开发机器学习模型,从NIR光谱中预测关键的土壤特性.
- 为土壤监测和管理应用提供有价值的资源.
- 为了使光谱数据在土壤科学中得到更广泛的使用.
主要方法:
- 从2106个矿物土壤样本收集的NIR光谱数据.
- 在R.中使用立方体和局部最小平方回归 (PLSR) 算法开发了预测模型.
- 利用了两个建模策略:平均光谱扫描和使用跨设备复制扫描.
主要成果:
- 机器学习模型被创建用于预测土壤有机碳 (SOC),pH,散装密度 (BD),碳酸盐 (CaCO3),可交换 (例如. K),沙子,泥和粘土含量.
- 评估了开发的立方体和PLSR模型的内部性能.
- 干光谱和立体模型是公开可用的下载.
结论:
- 开发的光谱数据集和预测模型为土壤性质评估提供了强大的工具.
- 这种资源有助于土壤监测和精准农业的进步.
- 对数据和代码的开放访问促进了土壤光谱学的合作研究.
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