Soil Urea Analysis Using Mid-Infrared Spectroscopy and Machine Learning

Abstract

Urea is the most common fertilizer used by the farmers. In this study, the variation of mid-infrared transmittance spectra with addition of urea in soil was studied for five different concentrations of urea. 150 gm of soil is taken and dried in a hot air oven for 5 h at 80°C and then samples are prepared by adding urea and water to it. The spectral signature of soil with urea is obtained by using an Infrared Spectrometer that reads the spectra in the mid infra-red region. The analysis is done using Partial Least Square Regression and Support Vector Machine algorithms by applying Savitzky Golay filter and Gaussian filter. The score plot, prediction and reference plots are used in the analysis using PLSR. RMSE and R-squared value are obtained from the analysis. It is evident that the detection accuracy was appropriate for Gaussian filter compared to Golay filter for both the PLSR and SVM models. The RMSE for PLSR is 0.8% and for SVM is 16%. The results show that Support vector machine model has higher accuracy compared to Partial least square regression model considering the prediction for which R square value is 0.99 with and without filters. SVM model gives better prediction without filters.