Abstract: Chrysanthemum tea has been one of the most popular food products, due to the health benefits and high commercial value. The medicinal and economic chrysanthemum can greatly vary in the different flowering stages. Among them, the flowering stages of Chrysanthemum can be categorized into the Jumi (flower buds), Taiju (flower buds just before blooming), and Duohua (fully bloomed flowers). At the same time, the chrysanthemum is required for the best picking time. However, manual picking cannot fully meet the requirement of large-scale production at the early flowering stage, due mainly to the labor-intensive and time-consuming. Untimely picking or picking errors at the different flowering stages can also lead to the waste of chrysanthemum, even the serious economic losses. Therefore, the picking robot can be expected to realize the accurate and rapid recognition of chrysanthemum in different flowering stages using lightweight model. In this study, an improved YOLOv8s model (YOLOv8s-RDL) was proposed for the object detection of chrysanthemum. Firstly, the C2f (faster implementation of CSP bottleneck with 2 convolutions) in Neck network was replaced by RCS-OSA (one-shot aggregation of reparameterized convolution using channel shuffle). The features were extracted to more efficiently fuse by the Backbone layer; Secondly, the decoupled head was replaced with the Dyhead (dynamic head), and then integrated into the DCNv3 (deformable convolutional networks v3). The multi-head self-attention mechanism was combined to strengthen the expression of the target detection head. Finally, the LAMP (layer-adaptive magnitude-based pruning) was used to reduce the number of parameters and the complexity of the model network. The amount of calculation was significantly reduced to maintain a high average precision level of the improved model. A comparison was also made to explore the effect of RCS-OSA in the different positions of the network. The performance of the model was depended mainly on the different pruning conditions and rates in the same pruning direction. The best improved model was obtained after network structure improvement and pruning. The best solution was attributed to the replacement of the C2f with the RCS-OSA only in the Neck part, particularly in the pruning condition of the adaptation model. The ablation experiments show that the mean average accuracies of the improved model for Duohua, Taiju and Jumi were 99.0%, 97.7% and 96.3%, respectively, in the classification and detection of flowering stage, which were 0.3, 1.9 and 3.8 percentage points higher than that of YOLOv8s. The mean average precision, precision and recall of the YOLOv8s-RDL for the Jumi, Taiju, and Duohua were 97.7%, 96.5%, and 95.2%, respectively, which were 1.9, 5.2 and 6.4 percentage points higher than YOLOv8s baseline model, respectively. The size of model weight also decreased by 6 MB. The improved model was greatly reduced the number of parameters and weight size, indicating the high detection accuracy. The mean average accuracies of YOLOv8S-RDL were 35.3, 2.9, 3.4, 1.9, 3.8 and 1.7 percentage points higher than those of SSD, YOLOv5s, YOLOv6s, YOLOv8s, Ginger-YOLOv5s and MSC-YOLOv8, respectively. At the same time, there were the smallest weight size and parameters of the improved model. In addition, the superiority of the algorithm was verified to introduce the TIDE (toolkit for identifying detection and segmentation errors) indicator. The detection errors of classification and background were reduced by 0.55 and 1.26, respectively, in the YOLOv8s-RDL, compared with the YOLOv8s. The better performance of detection and classification was achieved to reduce the influence of background and interference factors. The improved model was also fully met the requirements of accurately and rapidly distinguish the chrysanthemum flowering stages. This finding can also provide the theoretical reference and technical support to realize the automatic picking of chrysanthemum in the various flowering period.