Accurate weed detection in soybean fields is essential for enhancing crop yield and reducing herbicide usage. This study proposes a YOLO-SW model, an improved version of YOLOv8, to address the challenges of detecting weeds that are highly similar to the background in natural environments. The research stands out for its novel integration of three key advancements: the Swin Transformer backbone, which leverages local window self-attention to achieve linear O(N) computational complexity for efficient global context capture; the CARAFE dynamic upsampling operator, which enhances small target localization through context-aware kernel generation; and the RTDETR encoder, which enables end-to-end detection via IoU-aware query selection, eliminating the need for complex post-processing. Additionally, a dataset of six common soybean weeds was expanded to 12,500 images through simulated fog, rain, and snow augmentation, effectively resolving data imbalance and boosting model robustness. The experimental results highlight both the technical superiority and practical relevance: YOLO-SW achieves 92.3% mAP@50 (3.8% higher than YOLOv8), with recognition accuracy and recall improvements of 4.2% and 3.9% respectively. Critically, on the NVIDIA Jetson AGX Orin platform, it delivers a real-time inference speed of 59 FPS, making it suitable for seamless deployment on intelligent weeding robots. This low-power, high-precision solution not only bridges the gap between deep learning and precision agriculture but also enables targeted herbicide application, directly contributing to sustainable farming practices and environmental protection.

Aiming to improve the existing corn cylindrical drum threshing process, where the high-frequency impact of threshing elements and rubbing actions result in a high kernel breakage rate, a radial telescopic drum taper adjustment method was proposed, and a corn conical threshing drum was developed. The threshing process was analyzed by combining theory and simulation, and the conical drum enabled rapid threshing and timely penetration of the grains through the sieve, reduced the damage caused by high-frequency action on the grains, and improved the uniformity of the distribution of the discharged material. The response surface test was carried out with the drum speed, feeding quantity, and large end diameter of the drum as the influencing factors and the damage rate as the evaluation index. The results showed that when the rotating speed of the drum was 340 r/min, the feeding quantity was 2.277 kg/s, the diameter of the big end was 506.7 mm, and the breakage rate was 2.826%. The threshing effect was found to be better than when using a cylindrical drum. The research results can provide a reference for low-damage and high-efficiency harvesting of grain.

A synchronous-profiling, low-position-cutting, flexible-reel-belt-conveyor, low-loss soybean header was designed to address the problems of a lack of soybean harvesting machines supporting soybean-corn strip intercropping, the few existing soybean headers, and the high loss rate of soybean headers. By establishing a dynamic model of the synchronous-profiling cutting device, the key structure and operation parameters that affect the performance of synchronous profiling were determined, while the key parameters of the flexible-reel-belt conveyor device were determined by theoretical analysis. Based on ADAMS rigid-flexible coupling, simulation analysis was conducted on the working process of the synchronous-profiling cutting device, verifying that the profiling cutting device can effectively control the height of the cutter off the ground with undulating ground and that the cutting device can accurately and quickly respond to ground excitation, meeting the requirements of synchronous-profiling, low-position cutting. Field tests showed that the loss rate and stubble height of the soybean headers are 1.34% and 70.36 mm, respectively, which are 55% and 22.7% lower than the existing reel-type rigid soybean headers, meeting the actual production requirements. This study can provide a reference for the structural design of soybean harvesting headers and the reduction of header losses.

Considering the problems of the low mechanical work rate and the high picking damage rate of baffle roller bionic picking devices in the harvesting process of fresh corn in China, a method of fresh corn harvesting based on dislocation baffle roller bionic picking is proposed. When the picking device is in operation, the dislocation baffle roller assists with picking by applying deflecting torque to the corn cob. The mechanical properties of the bottom kernel were significantly better than those of the top kernel, according to the results of a triaxial compression test on fresh corn kernels, and the force applied by the picking device from the bottom kernel’s side could successfully prevent cob breakage. To determine the optimal combination of operating parameters for the bionic picking device, a three-factor, three-level virtual response surface optimization test was conducted using Box–Behnken’s central combination method with the baffle roller tilt angle, the baffle roller gap, and the stalk speed as the test factors, and the maximum contact force as the test indexes. Based on the theoretical analysis results, a test bench of the dislocation baffle roller bionic picking device was made. When the picking damage rate was 0.32%, the baffle roller dislocation was 5 mm, the baffle roller tilt angle was 41°, the baffle roller gap was 25 mm, and the stalk speed was 338 mm/s. This study can provide a reference for researching low-damage picking of fresh corn cob.

In response to the issues that existing corn headers are not only unsuitable for harvesting under the strip intercropping mode of soybeans and corn but also have the problems of being heavy and causing high harvest losses, a new type of header has been designed. This new header is lighter, reduces harvest losses, and is specifically adapted for use in the strip intercropping mode of soybeans and corn. By analyzing the corn stalks and the growth conditions of the corn, efforts were made to reduce the loss rate of kernels during harvesting, leading to a structural design of the header frame tailored for the strip intercropping mode. Following this, finite element analysis was employed to achieve a lightweight design of the header. The results show that the optimized header is 28.4 kg lighter than the original. The optimal working parameter combination for the combine harvester includes a forward speed of 0.94 m/s, a stalk roll rotational speed of 566.5 r/min, and a kernel moisture content of 25%, under which conditions the grain loss rate of the corn header is 0.32% and the ear loss rate is 1.1%. The development of this specialized corn header is conducive to enhancing both the quality and efficiency of mechanized harvesting in the strip intercropping mode of soybeans and corn.

This study establishes a sensitivity evaluation system based on the E-TOPSIS method and combines it with the MORIME algorithm for the optimization design of the frame. First, a three-dimensional model and a finite element analysis model of the frame were developed. The loading conditions of the frame were then analyzed, followed by static and modal analyses. Modal data of the frame were also extracted. The experimental results prove the reliability of the established finite element model and the subsequent optimization results. A sensitivity evaluation system based on the E-TOPSIS method was established in this study. Using this system, the sensitivity of the frame components with respect to three different performance parameters was analyzed, enabling the scientific and rapid selection of 17 design variables and significantly reducing the optimization workload. The experimental design was then conducted using Latin hypercube sampling and CCD sampling methods. Finally, the multi-objective lightweight design of the selected components was performed based on the MORIME algorithm. After optimization, the stress increased by 12.01% and 1.52% under two operating conditions, while deformation increased by 0.647 mm and 0.607 mm, and the frame mass was reduced by 22.754 kg, a decrease of 12.8%. The experimental results demonstrate the effectiveness of the proposed method.