Accurate estimation of the number of emerged soybean seedlings is critical for field production management. However, existing methods struggle to achieve rapid and precise counting due to several challenges: variation among varieties, differences in growth stage, large variations in object scale, and complex backgrounds. To address these challenges, we constructed a dataset of UAV images of multiple soybean varieties at the seedling stage and developed a lightweight detection model named SSDNet. The main innovations of this model include replacing the YOLOv11n backbone with StarNet to improve performance while maintaining efficiency; introducing a C3k2-PFDConv module to extract fine-grained features at multiple scales; incorporating a lightweight shared convolution detection head to reduce computational redundancy; and adopting slicing-aided inference to process high-resolution imagery. Experimental results show that SSDNet achieves 98.7% mAP@50 with only 1.57 M parameters and an inference time of 5.4 ms per image. When combined with SAHI, it completes detection in high-resolution imagery covering 675 breeding plots in 9.8 min. Across the 300 validation plots, the method achieved an absolute counting error of no more than 7 seedlings per plot relative to manual field counts. In summary, SSDNet enables accurate and efficient soybean seedling counting under the breeding field conditions and UAV image acquisition protocol tested in this study, providing technical support for emergence assessment in breeding fields.