The estimation of tobacco leaf yield is a crucial task, as the number of leaves directly impacts the yield. Traditional manual statistics are inefficient and costly, in order to solve these problems, this research proposes a lightweight YOLOv8-SLSS tobacco leaf counting detection algorithm, which improves on the YOLOv8n methods for the lack of detection accuracy, high computational complexity, and missed detections caused by overlapping tobacco leaves. The algorithm replaces the original model′s backbone network with an enhanced ShuffleNetV2light architecture, reducing model parameters and computational load. Integrate the LHCB module into the neck network′s C2f module to expand the model′s receptive field, enhances detection capabilities and reduces computational load. The introduction of the SEAMDetect module has enhanced the detection capabilities in scenarios involving occlusion by tobacco leaves. Finally, the SPPELAN module is introduced to enhance the model multi-scale feature extraction capability and computational efficiency. Experimental results demonstrate that the modified model significantly reduces model parameters and floating-point operations by 63.3% and 61.7% respectively. The algorithm′s average precision improves from 91.8% to 93.1%, achieving a real-time detection speed of 83 fps, marking a 51% enhancement over the original algorithm, meeting real-time detection demands. The improved algorithm enhances the detection ability of the traditional YOLO model in tobacco leaf occlusion scenarios, realizes the balance of high accuracy, lightweight design, and real-time detection performance, thus providing effective technical support for the digitization of tobacco agriculture.