To effectively identify tea buds in complex environments and improve the precision of intelligent harvesting while minimizing damage to tea trees, this study addresses the issues of low detection accuracy and poor robustness exhibited by traditional target detection algorithms in tea gardens, and proposes YOLOv7-tea model for tea bud identification and detection based on an improved YOLOv7, so as to achieve rapid recognition and detection of tea buds. First, tea bud images were collected and annotated, and data augmentation was performed to construct a tea bud dataset. Next, the CBAM attention mechanism module was introduced into three feature extraction layers of the YOLOv7 backbone network to enhance the model's feature extraction capability; the SPD-Conv module was used to replace the SConv module in the neck network's downsampling module to reduce the loss of small object features; and the EIoU loss function was employed to optimize box regression, thereby improving the accuracy of the predicted boxes. Finally, a comparative experiment was conducted between other target detection models and the YOLOv7-tea model using the tea bud image dataset as a sample, and the recognition effect of tea buds shot at different distances and angles was tested. The experimental results show that the YOLOv7-tea network model outperforms the YOLOv7 model in terms of precision (P), recall (R), and mean average precision (mAP) by 2.87, 6.91, and 8.69 percentage points, respectively. Additionally, the model has a faster detection speed and exhibits higher confidence scores in the recognition and detection of tea buds in complex backgrounds. The YOLOv7-tea model constructed in this study demonstrates better recognition performance for small-sized tea leaf buds, reducing instances of missed detection and false alarms. It exhibits good robustness and real-time performance, offering valuable insights for estimating tea yield and implementing intelligent harvesting.