用机器学习分析土壤近红外光谱，快速精准测定碳氮含量。
Spectroscopy Analysis with Machine Learning Regression for the Quantification of Carbon and Nitrogen Contents in Inceptisol and Oxisol Soil Types: Comparing Different Preprocessing and Validation methods as well as Feature Importance
- 结合SG滤波与鲁棒去噪，提升光谱数据质量。
- 模型在两种土壤中均实现RPD>2.0，误差极低且无过拟合。
- 适合农业监测、土壤肥力评估等需要快速检测的场景。
近红外（NIR）光谱技术作为传统土壤分析的替代方法，具有快速、低成本和非破坏性优势。本文利用便携式MyNIR设备采集的NIR光谱数据，采用机器学习方法构建碳（C）和氮（N）含量预测模型，针对氧化土（Oxisols）和初成土（Inceptisols）两类土壤。比较了多种预处理方法，最优为Savitzky-Golay滤波与基于非线性迭代偏最小二乘法（NIPALS）结合Huber损失函数的异常值剔除法。对比10折交叉验证、留一法及肯纳德-斯通法保留样本的保持法，并进行标准化处理。采用集成学习策略，以偏最小二乘法（PLS）、支持向量回归（SVR）和岭回归为基模型，线性回归为元模型。通过决定系数（R²）、均方根误差（RMSE）、平均绝对误差（MAE）和性能偏差比（RPD）评估模型性能。结果显示，不同土壤类型间存在性能差异，受成土特性影响；但整体模型均达到RPD > 2.0，表明其具备高精度与低过拟合特征，适用于快速碳氮定量。该方法有助于优化可持续农业实践，支持生产者与顾问基于有机质含量、肥力指标与养分可用性做出更高效决策。
原文摘要 · Abstract (English)
Near-Infrared (NIR) spectroscopy has emerged as a promising alternative to traditional soil analysis methods, offering advantages such as speed, low cost, and non-destructive testing. This work proposes a machine learning (ML) approach to calibrate predictive models for carbon (C) and nitrogen (N) content in Oxisols and Inceptisols, utilizing NIR spectral data acquired with a portable MyNIR device. Various preprocessing methods were evaluated, with the most effective being the Savitzky-Golay (SG) filter and a robust outlier removal method based on the Nonlinear Iterative Partial Least Squares (NIPALS) algorithm coupled with a Huber loss function. Multiple validation strategies were compared, including 10-fold cross-validation, leave-one-out, and holdout via the Kennard-Stone method, followed by standardization. Stacking ensemble learning models were employed, using Partial Least Squares (PLS), Support Vector Regression (SVR), and Ridge as base models, with linear regression as the meta-model. The models were evaluated using R2, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Ratio of Performance Deviation (RPD) metrics. The performance gap between soil types suggests the influence of pedological characteristics. Furthermore, the models achieved an RPD > 2.0 with low overfitting, validating the potential of this approach for rapid C and N quantification. This study contributes to the optimization of sustainable agricultural practices, aligning with the demand for efficient and environmentally friendly analytical methods. The developed technique enables faster decision-making for producers and consultants based on organic matter content, fertility indicators, and nutrient availability.
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