【摘要】 植株精确计数在精准化农业中至关重要，是监测作物生长和预测产量的重要基础。针对成熟期烟草植株存在的密植、重叠和高空小目标等难题，研究提出了一种轻量级GEW-YOLOv8烟株检测算法。该算法采用GhostC2f模块减少了模型的参数和计算量，并应用高效的多尺度注意力机制来区分被遮挡的烟草植株。此外，还引入了WIoU损失函数，以加速模型收敛并提高准确性。实验结果表明，与原始模型相比，模型的效率和准确性有了显著提高，浮点运算次数减少了24.7%,模型大小减少了26.7%。改进后的模型烟草植株检测平均精度AP_0.5和AP_0.5～0.95分别为99.1%和86.2%,相较于原YOLOv8n模型分别提高了0.8%和3.6%。改进后的模型能够更快、更精确地识别田间烟草植物，为智慧烟草农业提供技术支持。更多还原

【Abstract】 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 AP_0.5 and AP_0.5～0.95 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.更多还原