Improved selected soil properties predictions using MIR and pXRF sensor fusion

doi:10.1016/j.jia.2025.09.028

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Junwei Wang¹^{, 2}, Qi Zou¹^{, 2}, Huimin Yuan¹^{, 2#}

¹College of Resources and Environmental Sciences/National Academy of Agriculture Green Development / Key Laboratory of Plant-Soil Interactions, Ministry of Education/China Agricultural University, Beijing 100193, China

²National Observation and Research Station of Agriculture Green Development, Quzhou 057250, China

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摘要

及时、准确地获取土壤养分信息对保障全球粮食安全和农业可持续发展至关重要。本研究利用中红外光谱（MIR）和便携式X射线荧光光谱（pXRF）两种近地传感技术，通过四种传感器融合策略：直接融合（DC）、基于稳定性竞争性自适应重加权采样（sCARS）和最小绝对收缩和选择算子（LASSO）算法的特征融合（sCARS-C、LASSO-C）、基于顺序正交偏最小二乘法（SO-PLS）算法的多模块融合和基于Granger-Ramanathan模型平均（GRA）算法的决策融合，以提升13种土壤属性的预测精度。研究结果表明：单一传感器模型（MIR或pXRF）可准确预测土壤有机质（SOM）、全氮（TN）、有效磷（AP）、钙（Ca）、铁（Fe）、锰（Mn）和pH（Rp²≥0.78, RPDp≥2.13），但对全钾（TK）、镁（Mg）、铜（Cu）、锌（Zn）、速效钾（AK）和全磷（TP）（Rp²≤0.75, RPDp≤1.99）的预测能力有限。DC模型显著提升了Mg（Rp²=0.76, RMSEp=358.76 mg kg^-1, RPDp=2.03）和TK（Rp²=0.75, RMSEp=775.96 mg kg^-1, RPDp=2.00）的预测精度。LASSO-C模型对AP、AK、TP、Zn、Mn和Cu的预测精度优于DC模型，其中AP（Rp²=0.89, RMSEp=21.37 mg kg^-1, RPDp=3.01）和Zn（Rp²=0.80, RMSEp=9.88 mg kg^-1, RPDp=2.32）为最优结果，这归因于LASSO算法有效地从MIR和pXRF全光谱数据中提取了特征变量，提高了预测精度。GRA模型对TP、pH、AK和Cu的预测精度最高，其Rp²值分别为0.80、0.82、0.82和0.65，RMSEp值分别为129.21 mg kg^-1、0.13、48.38 mg kg^-1和3.87 mg kg^-1，RPDp值分别为2.23、2.34、2.37和1.67。考虑到成本效益，推荐使用MIR光谱预测SOM、TN和Ca（Rp²≥0.88, RPDp≥2.87），使用pXRF光谱预测Ca、Fe和Mn（Rp²≥0.80, RPDp≥2.22）。因此，本研究证明了MIR与pXRF传感器融合在提升关键土壤养分预测精度的有效性，尤其对土壤有效养分和微量元素。

Abstract

The timely and accurate assessment of soil nutrient information is essential for ensuring global food security and sustainable agricultural development. This study evaluated the individual and fusion performance of mid-infrared (MIR) and portable X-ray fluorescence (pXRF) spectroscopy for predicting selected soil properties. Four sensor fusion strategies were implemented: direct concatenation (DC), feature-level fusion using stability competitive adaptive reweighted sampling (sCARS) and least absolute shrinkage and selection operator (LASSO) algorithms (sCARS-C and LASSO-C), multi-block fusion via sequential orthogonal partial least squares (SO-PLS), and Granger-Ramanathan model averaging (GRA) method to enhance prediction accuracy for 13 soil properties. The findings revealed that single sensor models using either MIR or pXRF provided accurate estimations for soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), calcium (Ca), iron (Fe), manganese (Mn), and pH, but showed limitations for total potassium (TK), magnesium (Mg), copper (Cu), zinc (Zn), available potassium (AK), and total phosphorus (TP). The DC model significantly improved predictions for Mg (Rp²=0.76, RMSEp=358.76 mg kg^-1, RPDp=2.03) and TK (Rp²=0.75, RMSEp=775.96 mg kg^-1, RPDp=2.00). The LASSO-C model demonstrated superior prediction accuracy compared to the DC model for AP, AK, TP, Zn, Mn, and Cu, achieving optimal results for AP (Rp²=0.89, RMSEp=21.37 mg kg^-1, RPDp=3.01) and Zn (Rp²=0.80, RMSEp=9.88 mg kg^-1, RPDp=2.32). This enhancement is attributed to LASSO's effective selection of feature information from the complete MIR and pXRF spectra. The GRA models achieved the highest prediction accuracy for TP, pH, AK, and Cu, with Rp² values of 0.80, 0.82, 0.82, and 0.65, RMSEp values of 129.21 mg kg^-1, 0.13, 48.38 mg kg^-1, and 3.87 mg kg^-1, and RPDp values of 2.23, 2.34, 2.37, and 1.67, respectively. For single-sensor applications, MIR spectra are recommended for predicting SOM, TN, and Ca (Rp²≥0.88, RPDp≥2.87), while pXRF is more cost-effective for measuring Ca, Fe, and Mn (Rp²≥0.80, RPDp≥2.22). This research demonstrates the effectiveness of MIR and pXRF sensor fusion in enhancing soil nutrient assessment accuracy, particularly for available nutrients and micronutrients.

Keywords: soil available nutrients micronutrients multi-block fusion feature-level fusion model averaging

Online: 26 September 2025

Fund:

This paper was supported by the National Key Research and Development Program of China (2023YFD1900104).

About author: Junwei Wang, E-mail: wangjw272@cau.edu.cn; #Correspondence Huimin Yuan, E-mail: hmyuan@cau.edu.cn

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Junwei Wang, Qi Zou, Huimin Yuan. 2025. Improved selected soil properties predictions using MIR and pXRF sensor fusion. Journal of Integrative Agriculture, Doi:10.1016/j.jia.2025.09.028

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