Purpose

Visible-near-infrared spectroscopy (VIRS) is one of the most promising alternative techniques for soil organic matter (SOM) due to its direct response. In this study, partial least squares regression (PLSR), support vector machine (SVM), artificial neural networks (ANNs), and Cubist combined with VIRS were utilized to develop the calibration model and evaluate the ability of machine learning models to predict soil organic matter content.

Materials and methods

A total of 190 surface soil samples (earth-cumulic-orthic anthrosols) were collected from the Weihe Plain of Shaanxi Province, China. The Kennard–Stone (KS) algorithm was employed to divide them into calibration and validation data. Moreover, the successive projections algorithm (SPA), competitive adaptive weight weighting algorithm (CARS), and their combination (SPA + CARS) were utilized to select characteristic wavelengths and improve the predictive ability of the model. Different evaluation indices, including root mean square error (RMSE), coefficient of determination (R²), the ratio of the performance to deviation (RPD), and the ratio of performance to interquartile range (RPIQ), were adopted to evaluate the accuracy of the model.

Results and discussion

In all cases, the AFS-SPA + CARS-Cubist method outperformed the PLSR, SVM, and ANN. For the Cubist model, the Rv², RPD, and RPIQ ranged from 0.8629 to 0.9782, 0.8720 to 3.0203, and 2.005 to 4.4164, respectively. According to the results, combining VIRS with Cubist could accurately determine the SOM of earth-cumulic-orthic anthrosol soils of the Weihe Plain, China. Furthermore, SPA + CARS provided more precise calibration–validation models than SPA and CARS.

中文翻译：

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结合机器学习和VIRS预测土壤有机质

目的

可见-近红外光谱法（VIRS）由于其直接响应，是土壤有机质（SOM）最具前景的替代技术之一。在这项研究中，偏最小二乘回归（PLSR），支持向量机（SVM），人工神经网络（ANN）和Cubist与VIRS结合用于开发校准模型并评估机器学习模型预测土壤有机物的能力物质含量。

材料和方法

从陕西省渭河平原总共采集了190个表层土壤样品（土-积-人为邻邦的人为土壤溶胶）。使用Kennard-Stone（KS）算法将其分为校准和验证数据。此外，利用连续投影算法（SPA），竞争自适应权重加权算法（CARS）及其组合（SPA + CARS）来选择特征波长并提高模型的预测能力。采用不同的评估指标，包括均方根误差（RMSE），确定系数（R ²），性能与偏差之比（RPD）和性能与四分位数范围之比（RPIQ），以评估准确性模型的

结果和讨论

在所有情况下，AFS-SPA + CARS-Cubist方法均优于PLSR，SVM和ANN。对于Cubist模型，Rv ²，RPD和RPIQ的范围分别为0.8629至0.9782、0.8720至3.0203和2.005至4.4164。结果表明，将VIRS与Cubist结合使用可以准确地确定渭河平原土壤-累积-正交的人为土壤的SOM。此外，与SPA和CARS相比，SPA + CARS提供了更精确的校准验证模型。