基于PCA和PLSR的机器学习模型用于预测异质土壤中的-N含量,使用近红外光谱学
Damiano Crescini1, Gabriele Mascialino1, Nicola Moggia1
1Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
使用多变量模型的近红外 (NIR) 光谱学提供了一种评估土壤的快速方法. 第一个衍生 (FD) 模型显示了在不同类型的土壤中对检测的优越预测能力.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 准确和快速的土壤气评估对于有效的农业管理至关重要.
- 传统的土壤分析方法可能耗时且劳动密集.
研究的目的:
- 评估近红外 (NIR) 光谱学与多变量分析结合用于量化土壤的有效性.
- 为了比较不同的光谱预处理技术和回归模型,以实现最佳的检测.
主要方法:
- 使用近红外 (NIR) 光谱在六种土壤类型上,具有不同的-N肥料含量.
- 应用光谱预处理技术,包括Savitzky-Golay过和衍生光谱学 (第一和第二衍生).
- 开发了用于量化的部分最小平方回归 (PLSR) 模型,并通过校准和验证评估了模型性能.
主要成果:
- 基于第一导数 (FD) 和第二导数 (SD) 的PLSR模型在校准过程中都显示出高精度 (R2 > 0.9).
- 在验证过程中,与SD模型 (R2 = 0.65,RPD = 1.77) 相比,FD模型表现出更好的预测能力 (R2 = 0.77,RPD = 2.06).
- 该研究在多种土壤类型中实现了实时在线检测能力,计算成本低.
结论:
- 近红外 (NIR) 光谱学,加上多变量建模 (特别是FD-PLSR),是快速准确地评估土壤的有希望的工具.
- 开发的方法比传统的离线方法具有优势,为改善农业管理提供实时数据.
- 在不同类型的土壤中验证了模型性能,提高了它在各种农业环境中的实际适用性.
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