Use of Machine Learning Models for Prediction of Organic Carbon and Nitrogen in Soil from Hyperspectral Imagery in Laboratory

Abstract

Organic carbon and total nitrogen are essential nutrients for plant growth. The presence of these nutrients at acceptable levels can create an optimal environment for the development of crops of interest. The application of spectroscopic techniques and the use of machine learning algorithms have made it possible to calibrate models capable of predicting the number of elements present in the soil. One of these techniques is hyperspectral imaging, which captures portions of the electromagnetic spectrum where the materials present in the soil can be differentiated due to the vibrations of chemical bonds. The objective of this research is to use statistical models to predict OC and N in soils from hyperspectral images. Transformations were applied to spectral and chemical data and the models used were Random Forest (RF) and Support Vector Machine (SVM). To select the best model, the values of the coefficient of determination (), root mean square error of prediction (RMSEP), and the ratio of performance to deviation (RPD) were considered. For OC, the values found for the RF model were an of 0.87, an RMSEP of 0.10, and an RPD of 6.74; the SVM model presented an of 0.92, an RMSEP of 0.20, and an RPD of 3.56. For the variable N, the values found for the RF model were an of 0.79, an RMSEP of 0.03, and an RPD of 5.44; for the SVM model, they were an of 0.87, an RMSEP of 0.08, and an RPD of 2.76. The RF model showed a better fit for both variables. The SVM model also produced acceptable results. The results show that machine learning models are a good alternative for analysing soil-related variables.