Accurate plant counting is crucial in precision agriculture, forming a critical foundation for monitoring crop growth and predicting yield. To address challenges such as densely packed, overlapping, and aerial small targets of tobacco plants during the maturity stage, a lightweight GEW-YOLOv8 tobacco plant counting algorithm was proposed. The algorithm utilizes the GhostC2f module to reduce the parameters and computational workload of the model and employs an efficient multi-scale attention mechanism to discern occluded tobacco plants. Additionally, the WIoU loss function is introduced to accelerate model convergence and improve accuracy. Experimental results show a significant improvement in efficiency and accuracy compared to the original model, with a 24.7% reduction in FLOPs and a 26.7% decrease in model size. The improved model tobacco plant detection accuracy AP0.5 and AP0.5~0.95 reached 99.1% and 86.2% respectively, which were increased by 0.8% and 3.6% respectively compared with the original YOLOv8n model. The improved model can more swiftly and accurately identify field tobacco plants, providing technical support for intelligent tobacco agriculture.