Crop straw chopping and returning technology has gained global implementation to enhance soil structure and fertility, facilitating increased crop yield. Nevertheless, technological adoption faces challenges from inherent limitations in machinery performance, including poor chopping and returning quality and high energy consumption. Consequently, this review first presented a theoretical framework that described the mechanical properties of straw, its fracture dynamics, interactions with airflow, and motion characteristics during the chopping process. Then, based on the straw returning process, the chopping devices were classified into five types: the chopped blade, the chopping machine, the chopping device combined with a no-tillage or reduced-tillage seeder, the chopping and ditch-burying machine, the chopping and mixing machine, and the harvester-powered chopping device. Advancements in spreading devices were also summarized. Finally, six key directions for future research were proposed: developing an intelligent field straw distribution mapping system, engineering adaptive self-regulating mechanisms for chopping and returning equipment, elucidating the mechanics and kinematics of straw in the chopping and returning process, implementing real-time quality assessment systems for straw returning operations, pioneering high forward-speed (>8 km/h) straw returning machines, and establishing context-specific straw residue management frameworks. This review provided a reference and offered support for the global application of straw returning technology.

In view of the prominent problem that nonlinear vibration of a belt conveyor can easily occur during the vertical grain-conveyance process due to the coupling effect of airflow clamping and traction of the conveyor belt, which seriously affects the efficiency and stability of conveying materials by the belt conveyor, a method of solving the vibration analysis of the vertical lifting section of the bulk grain belt unloader by using nonlinear vibration is proposed. Firstly, based on the laminated plate theory, the vertical lifting belt and the grain material clamped by the belt are laminated. The nonlinear vibration differential equation of the vertical lifting section of the bulk grain-carrying ship unloader is established by elastic–plastic mechanics, and solved by perturbation theory and Galerkin discrete analysis. The vibration response curve and structural natural frequency of the vertical lifting section of the bulk grain-carrying machine are obtained by numerical solution, and the influence of the volume content of the clamped material on the vibration response and structural natural frequency of the lifting section is analyzed. This study provides theoretical support for the design of pressure-supply parameters, overall structure and operation parameters of the subsequent entrainment ship unloader, promotes the rapid development of the entrainment ship unloader, provides theoretical support for the design, manufacture, later operation, and maintenance of the entrainment ship unloader, and thus provides equipment and technical support for building an efficient and intelligent port.

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.

Grain kernel morphology strongly influences aerodynamic loads, breakage susceptibility, and processing performance. Conventional two-dimensional projection imaging and manual measurements lack the accuracy and completeness needed to represent the true three-dimensional structure of kernels. We present an open X-ray computed tomography dataset of wheat kernels that includes axial slice stacks and three-dimensional shape models for 100 individual kernels, together with per-sample morphometrics including principal-axis lengths, elongation index, flatness index, convexity, and sphericity. The dataset supports statistics of kernel shape, enables analysis of internal microstructure from the provided slice stacks, and facilitates high-fidelity discrete element modelling with realistic particle geometry.

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