Supervised Machine Learning Algorithms Can Predict Penetration Resistance in Mineral-fertilized Soils

Abstract

ABSTRACTIt is desirable for farmers and agricultural engineers to estimate the changes in soil penetration resistance (SPR) due to the usage of mineral fertilizers. However, reliable instruments for such measurements are not always available. In this study, the effects of conventional N, P, and K-based mineral fertilizers are firstly investigated on SPR. Then, machine learning methods are utilized to predict SPR. The cone index as an indicator of SPR was measured in different soil depths between 0 and 90 cm, different soil moisture contents from 10% to 30%, and different soil organic matter contents from 1% to 8%. Experimental data showed that fertilizing treatments were more effective on the SPR of soil samples (p < .05) in higher penetration depths. Double fertilizing of urea resulted in the highest SPR in the studied range of soil depth. Fertilizing treatments, soil moisture contents, soil organic matter contents, and penetration depths as input features of the samples were then trained to the machine using supervised learning methods and the machine’s performance in prediction of SPR was investigated. Results showed that support vector machine resulted in an acceptable performance, normalized MSE and R2 of which were equal to 0.009 and 0.98, respectively using Gaussian kernel with kernel parameter of 30. Findings of this study reveal that machine learning can reliably predict SPR when instruments such as cone penetrometer are not available.