Abstract
This study attempts to utilize newly developed machine learning techniques in order to develop a general prediction algorithm for agricultural soils in Saudi Arabia, specifically in the Taif region. Energy dispersive X-ray fluorescence (EDXRF) measurements were used to develop national predictive models that predict the concentrations of 14 micronutrients in soils of Taif rose farms, for providing high-quality data comparable to conventional methods. Machine learning algorithms used in this study included the simple linear model, the multivariate linear regression (MLR); and two nonlinear models, the random forest (RF) and multivariate adaptive regression splines (MARS). Our study proposes a machine learning (ML) strategy for predicting fertility parameters more accurately in agricultural soils using 10 farms of the Taif rose (Rosa damascena) in Taif, Saudi Arabia as a case study. Results demonstrated that MARS provides higher prediction performance when the number of explanatory variables is small, while RF is superior when the number of variables is large. On the other hand, the MLR is recommended as a moderate method for predicting multivariate variables. The study showed that multivariate models can be used to overwhelm the drawbacks of the EDXRF device, such as high detection limits and an element that cannot be directly measured.