Shaohao Zhou

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.

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.

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.