MIRS and XRF Data Fusion for Improving Soil Fertility Attributes Prediction

The evaluation of soil samples using spectral techniques provides a sustainable approach to soil health assessment, reducing reliance on traditional, waste-producing analytical methods. Over the past few decades, spectroscopic and spectrometric techniques have gained prominence in soil analysis due to their non-destructive nature. X-ray fluorescence (XRF) and mid-infrared spectroscopy (MIRS) are particularly valuable, as they provide complementary information on the elemental and molecular composition of soils, respectively. Both have been successfully combined with machine learning algorithms to model and predict soil fertility parameters as alternatives to conventional wet chemistry. This study explores the potential of data fusion between XRF and MIRS measurements to enhance soil fertility prediction accuracy. A total of 160 soil samples were analyzed using a Panalytical Epsilon 5 EDXRF spectrometer, employing four different secondary targets, and a Bruker Alpha II Fourier-transform mid-infrared spectrometer. Three machine learning models were trained on individual and fused datasets: Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Random Forest Regression (RF). The regression models built upon the fused data yielded increased performance for organic Carbon (OC), exchangeable Calcium (exCa), and exchangeable Potassium (exK). For OC, the RF model yielded the best performance, with the fused approach achieving a 5% reduction in RMSE and a 7% increase in RPD relative to standalone XRF. For exCa, RF was again the top-performing algorithm under fusion, providing a 25% reduction in RMSE and a 51% increase in RPD. For exK, the best results were obtained with PLS, which delivered a 16% reduction in RMSE and a 27% increase in RPD. These results demonstrate that integrating complementary spectral information from XRF and MIRS can enhance the prediction of key soil fertility attributes, offering a reliable and sustainable alternative to conventional chemical analyses. Beyond improving model accuracy, the proposed fusion framework highlights the potential of combining multi-sensor data to expand the applicability of spectral techniques for large-scale, rapid soil fertility assessment.