To solve the problems of complex tea bud detection scenarios in natural environments and the large number of model parameters that cannot be deployed on embedded devices, a lightweight tea bud detection method based on YOLOv8n is proposed. We construct a lightweight backbone network, MFBNet, introducing the MBConv module to significantly reduce model computation. Simultaneously, we incorporate the CBAM attention module into the backbone network to suppress irrelevant information, thereby enhancing the model′s detection accuracy. Furthermore, the introduction of the AKConv module improves the VoVGSCSPC structure, proposing the innovative AVCStem module, which replaces the C2f module in the neck network, further reducing model parameters and enhancing the efficiency of embedded device deployment. Finally, we employ the GSConv module to replace all Conv modules in the neck network structure, facilitating fast model computation and increasing the detection speed of tender tea buds. The results indicate that the proposed model in this paper outperforms the original YOLOv8n model with a 3.5% improvement in mAP50, 55.6% increase in FPS, and a 14.3% reduction in parameters. The model demonstrates strong robustness, meeting the requirements for lightweight and rapid detection of tender tea buds in complex scenarios.