A Novel Model for Soil Organic Matter and Total Nitrogen Detection Based on Visible/Shortwave Near-Infrared Spectroscopy

Jiangtao Qi, Panting Cheng, Junbo Zhou, Mengyi Zhang, Qin Gao, Peng He, Lujun Li, Francis Collins Muga and Li Guo ()
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Jiangtao Qi: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Panting Cheng: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Junbo Zhou: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Mengyi Zhang: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Qin Gao: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Peng He: Key Laboratory of Mollisols Agroecology, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China
Lujun Li: Key Laboratory of Mollisols Agroecology, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China
Francis Collins Muga: Department of Agricultural and Rural Engineering, University of Venda, Thohoyandou 0950, South Africa
Li Guo: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China

Land, 2025, vol. 14, issue 2, 1-23

Abstract: Soil organic matter (SOM) and total nitrogen (TN) are critical indicators for assessing soil fertility. Although laboratory chemical analysis methods can accurately measure their contents, these techniques are time-consuming and labor-intensive. Spectral technology, characterized by its high sensitivity and convenience, has been increasingly integrated with machine learning algorithms for soil nutrient monitoring. However, the process of spectral data analysis remains complex and requires further optimization for simplicity and efficiency to improve prediction accuracy. This study proposes a novel model to enhance the accuracy of SOM and TN predictions in northeast China’s black soil. Visible/Shortwave Near-Infrared Spectroscopy (Vis/SW-NIRS) data within the 350–1070 nm range were collected, preprocessed, and dimensionality-reduced. The scores of the first nine principal components after a partial least squares (PLS) dimensionality reduction were selected as inputs, and the measured SOM and TN contents were used as outputs to build a back-propagation neural network (BPNN) model. The results show that spectral data processed by the combination of standard normal variate (SNV) and multiple scattering correction (MSC) have the best modeling performance. To improve the accuracy and stability of this model, three algorithms named random search (RS), grid search (GS), and Bayesian optimization (BO) were introduced. The results demonstrate that Vis/SW-NIRS provides reliable predictions of SOM and TN contents, with the PLS-RS-BPNN model achieving the best performance ( R 2 = 0.980 and 0.972, RMSE = 1.004 and 0.006 for SOM and TN, respectively). Compared to traditional models such as random forests (RF), one-dimensional convolutional neural networks (1D-CNNs), and extreme gradient boosting (XGBoost), the proposed PLS-RS-BPNN model improves R 2 by 0.164–0.344 in predicting SOM and by 0.257–0.314 in predicting TN, respectively. These findings confirm the potential of Vis/SW-NIRS technology and the PLS-RS-BPNN model as effective tools for soil nutrient prediction, offering valuable insights for the application of spectral technology in sensing soil information.

Keywords: visible/shortwave near-infrared spectroscopy; soil organic matter; total nitrogen; dimensionality reduction; machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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