A Study of a Model for Predicting Pneumatic Subsoiling Resistance Based on Machine Learning Techniques

Abstract

In order to explore the drag reduction mechanism of pneumatic subsoiling and study the influence of pneumatic subsoiling on the soil, this study used machine learning models to predict the working resistance of a pneumatic subsoiler and adopted random forest (RF), error back-propagation (BP), eXtreme gradient boosting (XGBoost) and support vector regression (SVR) to analyze and compare the predictions of these four models. Field experiments were carried out in two fields with different bulk densities and moisture content. The effects of these parameters on the resistance of pneumatic subsoiling were studied by changing the working air pressure, depth and forward speed. In the RF, SVR, XGBoost and BP models, five parameters (working air pressure, working depth, forward speed, bulk density and moisture content) were inputted as independent variables, and the operating resistance of pneumatic subsoiling was used as the predicted value. After training the four models, the results showed that the R2 value of the RF model was the highest and the error was the smallest, which made it better than the SVR, XGBoost and BP models. The values of MAPE, R2 and RMSE for the RF model’s test set were 0.01, 0.99, and 3.61 N, respectively, indicating that the RF model could predict the resistance value of subsoiling well. When the RF model was used to analyze the five input parameters, the experimental results showed that the contribution of working air pressure to reducing the resistance of subsoiling reached 29%, indicating that pneumatic subsoiling can reduce the resistance, drag and consumption.

Intelligent Control Technology and System of on-Demand Irrigation Based on Multiobjective Optimization

“…The machine learning algorithm is very suitable for constructing multi-input and multi-output nonlinear system models, with differences in the principles of different types of machine learning leading to differences in model performance. This paper selects four kinds of machine learning models with good performance, including BP, SVR, RF and XG Boost [32][33][34][35].…”