Subsoiling has been acknowledged worldwide to break compacted hardpan, improve soil permeability and water storage capacity, and promote topsoil deepening and root growth. However, there exist certain factors which limit the wide in-field application of subsoiling machines. Of these factors, the main two are poor subsoiling quality and high energy consumption, especially the undesired tillage depth obtained in the field with cover crops. Based on the analysis of global adoption and benefits of subsoiling technology, and application status of subsoiling machines, this article reviewed the research methods, technical characteristics, and developing trends in five key aspects, including subsoiling shovel design, anti-drag technologies, technologies of tillage depth detection and control, and research on soil mechanical interaction. Combined with the research progress and application requirements of subsoiling machines across the globe, current problems and technical difficulties were analyzed and summarized. Aiming to solve these problems, improve subsoiling quality, and reduce energy consumption, this article proposed future directions for the development of subsoiling machines, including optimizing the soil model in computer simulation, strengthening research on the subsoiling mechanism and comprehensive effect, developing new tillage depth monitoring and control systems, and improving wear-resisting properties of subsoiling shovels.

The main causes of high productional costs and greenhouse gas emissions in the chopping process of maize straws are high energy consumption and breaking force. Addressing these issues, this paper proposes a solution that leverages machine-learning algorithms to select appropriate operational parameters for chopping devices, thereby reducing energy consumption and the cutting force. In this study, the peak breaking force of the stalk (PB), the energy consumption of the stalk chopping (EC) and the slide-cutting momentum of the disc blade (SM) were set as dependent variables, and the rotation speed of the Y-type blade (RSY), transmission ratio (TR) and slide-cutting angle (SA) were set as independent variables. Various techniques, including back-propagation (BP), a radial basis function (RBF), an artificial neural network (ANN), support vector regression and a stepwise polynomial regression model, were applied using a 6-fold cross-validation approach to determine the most effective predictive models. The results indicated that the BP-ANN model performs best in predicting the PB (R²_Test = 0.9860) and SM (R²_Test = 0.9561), while the RBF-ANN model yields the highest accuracy in predicting the EC (R²_Test = 0.9255) under the optimal parameters. Subsequently, a verification test was conducted using randomly selected training and testing data based on the selected predicted functions. The results demonstrated that the R²_Train and R²_Test data for PB, EC and SM are all above 0.95, indicating that the BP and RBF neural networks are capable of accurately predicting the nonlinear relationship between the dependent variables (EC, SM and PB) and independent variables (RSY, TR and SA) in practical applications.

Distribution properties of airflow in the full envelope type maize stalk chopping chamber