Comparative Analysis of Raw and Preprocessed Vis–NIR and MIR Spectra for Soil Property Estimation

Demand for rapid and cost-effective soil analysis has increased the use of spectroscopy, particularly in the visible–near-infrared (Vis–NIR) and mid-infrared (MIR) regions. Using 8304 soil samples from the United States Department of Agriculture spectral library, this study evaluated the effects of raw and preprocessed spectra on the prediction accuracy of eleven key soil properties across Vis–NIR and MIR regions using multiple machine learning algorithms. Spectral preprocessing, combining baseline correction and standard normal variate transformation, consistently improved prediction accuracy compared to the raw spectra. Overall, MIR-based models consistently outperformed Vis–NIR across all soil properties, with the largest performance gains observed for potassium, bulk density, and nitrate nitrogen. Among the machine learning approaches evaluated, artificial neural networks and categorical boosting algorithms provided the strongest and most consistent predictive performance across both spectral regions. These findings demonstrate that combining appropriate spectral preprocessing, spectral region selection, and advanced machine learning algorithms can substantially improve soil property prediction using spectroscopy.