Body tilt is typically unavoidable for agricultural vehicles or platforms when operated over tough terrain in agricultural production, and, therefore, it is not possible to guarantee the operation performance. Current leveling methods mainly focus on wheeled vehicles or adjustment for single working parts rather than the vehicle body, and few of them could be applied to crawler vehicles. The objective of this research was to put forward an adaptive leveling system for combine harvesters based on the development of a new four-point lifting adjustable crawler chassis. The working principle of the proposed adjustable mechanism was studied on the basis of the analysis of adjustment characteristics under multiple posture adjustment conditions in a RecurDyn environment. The design relies on the combination of the attitude detection of the leveling system and adjustment calculation of driving hydraulic cylinders according to the established mathematical models. Within the designed adjustable extent (including adjustment range of vehicle height 0–87.8 mm, lateral inclination ±3.98° and longitudinal inclination −2.9–5.2°), the leveling performance of lateral and longitudinal inclination was then tested separately. The experiment showed that the leveling system could achieve automatic leveling with an accuracy of ±0.4° and could provide technical support for the development of crawler vehicles.

The combine harvester equipped with attitude-adjustment functionality significantly enhances its adaptability to complex terrain but often struggles to maintain the reliability of its mechanisms. Therefore, investigating the dynamic load characteristics of the attitude-adjustment mechanism becomes imperative. This article employed the DEM–FMBD (Discrete Element Method–Flexible Multibody Dynamics) bidirectional coupling simulation method to establish a multibody dynamic model of a tracked combine harvester. The study delved into the interaction mechanism and dynamic stress response characteristics between the tracked chassis and the complex terrain under various height adjustments, lateral adjustment angles, longitudinal adjustment angles, and different field-ridge crossing methods. Finally, the accuracy of the coupled simulation model was validated through a constructed stress detection system. The research findings revealed that the displacement and tilt angle deviation of the hydraulic cylinders utilized to execute the chassis adjustment actions in the constructed coupled simulation model was less than 5%, and the deviation between the simulation results and the actual maximum dynamic stress under multiple working conditions ranged from 7% to 15%. This verification confirmed the effectiveness of the DEM–FMBD coupled simulation method. Under different adjustment conditions, the maximum stress position was consistently distributed in the same area of the left-front and left-rear rotating arms. The primary and secondary effects of the various parts of the adjustment mechanism on the overall reliability of the chassis were as follows: left front > right front > left rear > right rear. By implementing the middle height with the adjustment strategy, the dynamic stress extreme value of the adjustment mechanism can be effectively reduced by 21.98%, thereby enhancing the structural stability of the chassis.

Aimed at addressing the problems of the existing straw choppers on combine harvesters, such as a large cutting resistance and poor cutting effect, combined with bionic engineering technology and biological characteristics, a bionic model was used to extract the characteristics of the cutting blades of locusta migratoria manilensis’s upper jaw. A 3D point cloud reconstruction and machine vision methods were used to fit the polynomial curve of the blade edge using Matlab 2016. A straw-cutting process was simulated using the discrete element method, and the cutting effect of the bionic blade was verified. Cutting experiments with rice straws were conducted using a physical property tester, and the cutting resistance of straw to bionic blades and general blades was compared. On the whole, the average cutting force of the bionic blades was lower than that of the general blades. The average cutting force of the bionic blade was 18.74~38.23% lower than that of a smooth blade and 1.63~25.23% lower than that of a serrated blade. Similarly, the maximum instantaneous cutting force of the bionic blade was reduced by 2.30~2.89% compared with the general blade, which had a significant drag reduction effect. By comparing the time–force curves of different blades’ cutting processes, it was determined that the drag-reducing effect of the bionic blade lies in shortening the straw rupture time. The larger the contact area between the blade and the straw, the more uniform the cutting morphology of the straw after cutting. Field experiment results indicate that the average power consumption of a straw chopper partially installed with bionic blades was 5.48% lower than one with smooth blades, measured using a wireless torque analysis module. In this research study, the structure of the straw chopper of an existing combine harvester was improved based on the bionic principle, which reduced resistance when cutting crop straw, thus reducing the power consumption required by the straw chopper and improving the effectiveness and stability of the blades.

During the operation of the longitudinal axis flow threshing device of a combine harvester, the threshed materials form accumulations and blockages on both sides of the screen surface, severely affecting the harvesting process. To evenly distribute the materials on the screen and solve the blockage issue, a multi-wing curved combination centrifugal fan is designed to match the mass distribution of the threshed materials. The movement mechanism of rice threshed materials in the cleaning shoe of a longitudinal axis flow combine harvester is investigated using the coupled CFD-DEM simulation method. The cleaning efficiency and performance of the traditional straight-blade fan screen device and the newly designed cleaning device are compared and analyzed, and field tests are conducted. The results show that the trajectory of the threshed materials cleaned by the device equipped with the multi-wing curved combination centrifugal fan is consistent with the mass distribution of the materials separated by the longitudinal axis flow threshing device. The absolute value of the centroid velocity of the material group in the X/Y direction is greater than that of the traditional fan, indicating that the movement speed of the particle group in the optimized fan is greater than that of the traditional fan. Therefore, in the actual cleaning process, the optimized fan’s air flow distribution more effectively accelerates the movement speed of the threshed materials, increasing the amount of materials cleaned per unit time, thereby improving the cleaning efficiency. Field comparative tests show that the designed cleaning device reduced the cleaning loss rate by up to 25.00% and the impurity content rate by 32.20%, achieving efficient and low-damage cleaning of the combine harvester. The study demonstrates the effectiveness of the proposed method for evenly distributing the materials and provides important reference for the study of other piled particle distribution systems.

To develop a grain flow sensor for combine auger grain outlets, a combine auger elevator was evaluated as the research object. A multi-point distributed array-style differential grain flow sensor for rice and wheat has been developed and tested on three field crops, rice, wheat, and rapeseed. The open system flow test bench was designed to compare the effects of differential processing in the time and frequency domains, as well as different filtering methods on the pre-processing of the collected raw sensor signals. Moreover, a random forest algorithm-based flow regression model was constructed for rice, wheat, and rapeseed based on the comparison of the flow signals of different grains. A weighted multiple linear regression model was constructed as the control group, and both bench and field tests were conducted. The results show that the sensor designed in this study can meet the needs of on-line grain flow monitoring. Meanwhile, the field monitoring errors for rice, wheat, and rapeseed based on the random forest flow regression model were −6.42~8.23%, −7.21~5.71%, and −4.19~4.78%, respectively, significantly better than the control group. The universal array-style grain flow sensor developed in this study provides significant practical value for the promotion and development of precision agriculture.

The precise, real-time delineation of rice lodging areas constitutes a fundamental prerequisite for the adaptive operation of unmanned combine harvesters. However, existing deep learning methods struggle to resolve a critical limitation: achieving an optimal equilibrium between robust regional morphological perception—which is crucial for irregular lodging patterns—and the ultra-low computational overhead demanded by resource-constrained edge terminals. To address this specific constraint, StarNet-RiceSeg is proposed as a lightweight semantic segmentation network explicitly tailored for unmanned harvesters. Initially, the architecture incorporates the minimalist StarNet as its backbone. By leveraging the unique “Star Operation,” it implicitly maps features into a high-dimensional nonlinear space, thereby significantly augmenting feature discriminability while drastically curtailing computational overhead. Furthermore, to mitigate the misdetection issues stemming from the textural similarity between lodged and upright rice, the Rice Spatial Attention (RSA) module was designed. By intensifying feature interaction within the spatial dimension, this module steers the network to focus on the cohesive morphology of lodged regions while effectively suppressing background noise. Experiments conducted on a self-constructed high-resolution rice lodging dataset demonstrate that StarNet-RiceSeg achieves a mIoU of 94.42%, significantly outperforming mainstream models such as U-Net, DeepLabV3+, SegNet and HRNet. Notably, the model maintains a compact footprint with only 8.01 million parameters and a computational load as low as 9.32 GFLOPs. Following optimization with TensorRT, the system achieved a real-time inference speed of 32.51 FPS on the NVIDIA Jetson Xavier NX embedded platform. These results indicate that StarNet-RiceSeg provides a high-precision, low-latency solution for perceiving rice lodging areas in complex field environments, facilitating unmanned precision harvesting.