According at the characteristics of large subsoiling resistance and small subsoiling range, a pneumatic subsoiling mechanism was designed to disturb the soil with different air pressure. To achieve the purpose of pneumatic subsoiling, first, the aerodynamic model of subsoiling was established, and then the feasibility of the design was verified by experiments. Using the dynamic telemetry data of the sensor, the effects of tillage depth (25, 30, and 35 cm), pressure (4, 6, and 8 MPa), and working speed (2.5, 3.0 and 3.5 km·h−1) on traction resistance were analyzed. The test results showed that under the condition pneumatic subsoiling, the traction resistance was reduced by 7.28%–22.37%, and the soil disturbance coefficient was 55.49%. The effect of pneumatic subsoiling showed that it has met the design requirements. Pneumatic subsoiling not only improved the problem of small stress of traditional subsoiling but also increased the disturbance of gas to soil on the basis of traditional subsoiling, so as to achieve the effect of subsoiling and reducing resistance and consumption.
This paper reports research based on pneumatic subsoiling and the design of a pneumatic subsoiling mechanism to overcome the problems of high resistance and high energy consumption of subsoiling. By analysing soil‐specific resistance, soil disturbance rate and soil bulkiness under different air pressure conditions, it is concluded that pneumatic subsoiling can effectively break the soil plough pan and reduce resistance to subsoiling. In order to analyse the impact of air pressure on subsoiling, in this study, principal component analysis was used to analyse the pneumatic subsoiling disturbance parameters (working air pressure, working depth and working speed), and the test results show that the contribution of air pressure to subsoiling resistance and subsoiling disturbance surface reached 24% and 25%, respectively. An orthogonal test was used to analyse the specific resistance of subsoiling, and its significance coefficient is 0.95. Long short‐term memory neural networks (LSTM) and bidirectional long short‐term memory neural networks Bi‐LSTM. are used to predict the cracks on the disturbed surface of subsoiling. LSTM is a method to predict future occurrence using time series data, which can be used to predict the cracks on the disturbed surface of soil, while Bi‐LSTM network is an innovative computing paradigm, which learns bidirectional long‐term correlation between time step and sequence data, to predict the trend of fissures on the disturbed soil surface. The RSME of LSTM and Bi‐LSTM are 4.80 and 6.55, and their determinative factor R2 is 0.95 and 0.94 respectively, which indicates that LSTM and Bi‐LSTM can effectively predict the cracks of pneumatic subsoiling. By analysing the specific resistance of pneumatic subsoiling, it can be shown that pneumatic subsoiling can reduce subsoiling resistance and expand the disturbance surface of subsoiling so as to achieve the effects of subsoiling, drag reduction and reduction of fuel consumption.
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.
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