In order to address the issues of low recognition rates and slow detection speeds for cotton apical buds under complex environmental conditions, an improved YOLOv5s object detection model is proposed. Initially, data of cotton apical buds in complex cotton field environments were collected. Subsequently, a lightweight Hd-ShuffleNetv2 network module was integrated into the backbone of the model to reduce the number of model parameters and accelerate detection speed. Additionally, NLMA and BotNeT attention mechanism modules were incorporated into the neck of the model to enhance feature extraction capabilities for cotton apical buds, thereby improving the model's recognition accuracy. Finally, the EIoU loss function was employed to tackle recognition challenges in cases where the apical buds were partially occluded, further increasing the success rate of identification. In order to verify the practical effectiveness of the improved object detection model, tests were conducted on cotton apical bud samples. The test results indicated that the mean average precision of the improved YOLOv5s model reached 91%, 1 percentage point increase over the original YOLOv5s model, with an enhanced detection confidence. The improved object detection model meets the detection requirements for cotton laser topping machines in the field, and provides robust technical support for further research in cotton laser topping technology.