Modeling of soil penetration resistance using statistical analyses and artificial neural networks

an important factor for the evaluation of an agricultural system's sustainability is the monitoring of soil quality via its physical attributes. the physical attributes of soil, such as soil penetration resistance, can be used to monitor and evaluate the soil's quality. artificial neural networks (ann) have been employed to solve many problems in agriculture, and the use of this technique can be considered an alternative approach for predicting the penetration resistance produced by the soil's basic properties, such as bulk density and water content. the aim of this work is to perform an analysis of the soil penetration resistance behavior measured from the cone index under different levels of bulk density and water content using statistical analyses, specifically regression analysis and ann modeling. both techniques show that soil penetration resistance is associated with soil bulk density and water content. the regression analysis presented a determination coefficient of 0.92 and an rmse of 0.951, and the ann modeling presented a determination coefficient of 0.98 and an rmse of 0.084. the results show that the ann modeling presented better results than the mathematical model obtained from regression analysis.