Data fusion of Vis-NIR and pXRF with machine learning for predicting andic properties in volcanic soils
Journal
Computers and Electronics in Agriculture
Journal Volume
250
ISSN
0168-1699
Date Issued
2026-08
Author(s)
Abstract
Volcanic soils exhibit distinctive andic properties, including organic carbon (OC), bulk density (Bd), phosphate retention (Pret), and the sum of ammonium oxalate-extractable aluminum and half iron (Alo + 0.5Feo), and play an important role in agricultural production and ecological functions. However, conventional determination of andic properties relies on destructive, labor-intensive, and time-consuming wet chemistry analyses. Soil spectroscopic techniques such as visible and near-infrared (Vis-NIR) spectroscopy and portable X-ray fluorescence (pXRF) provide rapid and non-destructive alternatives. This study aimed to elucidate the relationships between andic properties and signals from Vis-NIR and pXRF, and to evaluate the accuracy of sensor-based models for predicting andic properties and soil classification. A total of 93 soil samples were collected from 24 pedons of volcanic soils (0–60 cm depth) in northern Taiwan, including Andisols and Inceptisols. Predictive models were developed using individual sensors and a data fusion approach calibrated with partial least squares regression (PLSR) and the Cubist algorithm. Both Vis-NIR and pXRF signals demonstrated associations with andic properties, while data fusion of these two sensors markedly improved model performance. In particular, the Vis-NIR + pXRF-based model calibrated with Cubist yielded good predictive performance, achieving R2 and LCCC values exceeding 0.90 for OC, Pret, and Alo + 0.5Feo, and R2 = 0.83 and LCCC = 0.89 for Bd. Moreover, 23 out of the 24 studied pedons were correctly classified. Integrating Vis-NIR and pXRF provided an efficient approach for assessing andic properties, improving management and resource-use efficiency in volcanic soils, and supporting sustainable smart agriculture. © 2026 Elsevier B.V.
Subjects
Andisols
Modeling
Soil classification
Soil health
Soil spectroscopy
Publisher
Elsevier BV
Description
Article number 111916
Type
journal article