The traction performance analysis of hydro-mechanical transmission (HMT) tractors is employed to evaluate and optimize the transmission system during the design phase, thereby reducing research and development costs associated with continuously variable tractor transmission systems. However, there is currently no established methodological framework for calculating the traction performance of HMT tractors. To address this gap, this study integrated traditional tractor traction performance calculation equations with a self-developed HMT energy consumption calculation method, thereby developing a traction performance calculation model for HMT tractors. Initially, the principle of the tractor’s hydrostatic power-split transmission system was introduced. Subsequently, mathematical models for calculating transmission system energy consumption and tractor traction performance were established, with key sub-models experimentally validated to ensure the reliability of subsequent results. On this foundation, a calculation method for the traction performance of HMT tractors was proposed. Finally, models before and after energy consumption optimization, as well as models under different road conditions and transmission system configurations, were utilized as comparative models to calculate the traction performance of HMT tractors under various settings. The results indicate that energy optimization of HMT not only improves the transmission performance and reduces fuel consumption of HMT tractors, but also enhances the matching capability of HMT tractors with farm tools under high traction efficiency. Additionally, road conditions significantly impact the traction performance of HMT tractors. In wheat stubble fields, the tractor’s traction performance is substantially lower than on standard roads, with a maximum traction force decrease of 40.8% at a slip rate of 30%.

Precise and real-time posture recognition of scallions during harvesting and post-harvest processing is critical for automated conveying, orientation adjustment, bundling, and packaging. Nevertheless, their slender and flexible form, varied spatial orientations, target overlap, lighting fluctuations, background interference, and constrained computational resources of edge devices present significant obstacles to reliable visual perception. This research introduces GOPD-YOLO, a lightweight oriented object-detection network built on the YOLOv8-OBB framework. The network integrates partial-convolution-based lightweight feature extraction to minimize redundant computation, large separable-kernel attention to boost long-range structural representation, and a shared detail-enhanced detection head to improve boundary- and orientation-sensitive prediction. A custom dataset comprising 1500 conveyor-belt images under diverse scallion posture scenarios was developed for model training and assessment. GOPD-YOLO attained a precision of 90.6%, a recall of 94.5%, an mAP@0.5 of 93.2%, and an mAP@0.5:0.95 of 71.5%, with 2.38 million parameters, 6.6 GFLOPs, and a model size of 4.9 MB. Relative to YOLOv8n-OBB, GOPD-YOLO enhanced recall by 3.8 percentage points while decreasing parameter count and model size by 22.7% and 22.2%, respectively. Deployment tests were performed on the Jetson Orin NX Super platform across varying conveyor speeds, lighting conditions, and scallion stacking levels to evaluate the model’s practical utility. These results indicate GOPD-YOLO’s potential as a lightweight vision-based solution for scallion posture recognition in automated harvesting and post-harvest processing.