Predicting silicon, aluminum, and iron oxides contents in soil using computer vision and infrared

Abstract Silicon, aluminum, and iron oxides are very abundant in soil. Their quantification is important for soil classification, which is a relevant information for the sustainable use and management of soils. In soil laboratories the determination of these oxides, using standard methods, is destructive, costly, laborious, and time consuming. This article presents two analytical methods to quantify SiO2, Al2O3, and Fe2O3 in soil samples using computer vision (COMPVIS) and mid-infrared spectroscopy (MIR). These two methods were developed using 52 soil samples from four states of Brazil. Digital images and MIR spectra were correlated with oxides contents quantified by atomic absorption spectroscopy (AAS) after acid digestion using three multivariate calibration methods: PLS, SPA-MLR, and LS-SVM. This the first time that soil image data has been correlated to silicon and aluminum oxides and the proposed method found excellent correlation values ( r 2 ranging from 0.95 to 0.99). With the exception of SiO2, MIR resulted in similar predictions to the COMPVIS method’s. LS-SVM presented r 2 higher than 0.95 for all oxides estimates. The developed analyses are low cost, fast, and environmentally sustainables.