Abstract: In order to solve the problem of difficult detection caused by the heavy occlusion and overlap of kiwifruit flowers at different flowering periods, this study proposes YOLOv8-KFP, a densely distributed kiwifruit flowering period detection model based on improved YOLOv8n. Firstly, taking the lightweight YOLOv8n as the benchmark model, StarBlock is used to improve the C2f of YOLOv8n, and the "Star operation" is used to enhance the ability of feature expression, so as to reduce the amount of model parameters and calculation and improve the ability of feature extraction. Secondly, the scale sequence feature fusion (SSFF) module and the triple feature encoder (TFE) module are introduced into the neck network to fuse the feature maps of different scales, so as to enhance the ability of the model to capture the multi-scale details and alleviate the problem of false detection and missing detection of blocked and overlapping kiwifruit flowers; The DySample upsampler is used to replace the traditional interpolation method to dynamically adapt to flower targets with different scales, shapes and boundaries, alleviate the fuzzy problem of dense flower edges, and improve the target positioning accuracy. Finally, the Soft-NMS post-processing algorithm is introduced to further optimize the detection effect of dense targets by adjusting the confidence of overlapping candidate boxes to reduce false deletion. 3 853 images of kiwifruit flowers densely distributed under natural conditions were collected and labeled according to the bud stage, half open stage, full open stage and withering stage. Combined with a variety of data enhancement strategies, 300 epochs of iterative training were completed on NVIDIA A40 GPU platform. Ablation experiments show that the use of C2f_Star module to improve the backbone network, the introduction of SSFF module and TFE module to improve the neck network, the use of DySample upsampler, and the use of Soft-NMS post-processing algorithm can respectively improve the performance of the model to a certain extent, and the improved module has a significant synergy. The final model achieved the precision (P) of 89.1%, the recall (R) of 88.7%, and the mean average precision (mAP) of 92.4% at an intersection over union (IoU) ratio of 0.5, while reducing the number of parameters and floating point of operations (FLOPs) to 2.66 M and 7.6 G, respectively. The improvement effect of the feature fusion network using SSFF module and TFE module and various feature fusion networks such as BiFPN, Slim-Neck, RepGFPN, HS-FPN on YOLOv8n were compared. The YOLOv8n using SSFF module and TFE module to improve the feature fusion network achieved the best effect in precision, recall and mAP_0.5, showing its excellent performance in multi-scale feature fusion of kiwifruit flowers. Compared with the mainstream lightweight target detection models SSD、YOLOv9t、YOLOv10n、YOLOv11n and YOLOv12n, the mAP_0.5 of YOLOv8-KFP is increased by 7.0, 4.0, 5.5, 4.2 and 4.1 percentage points respectively. Compared with the more complex YOLOv8s, YOLOv8-KFP is increased by 2.7 percentage points on mAP_0.5, the amount of FLOPs is reduced by 73.2%, and the amount of parameters is reduced by 76.1%, giving good consideration to the detection accuracy and the lightweight of the model. Moreover, the visualization results show that the improved model significantly reduces the missed detection rate in complex scenes such as branches and leaves occlusion and flowers occlusion, and can more accurately distinguish flower targets in different flowering periods, and has stronger robustness in the densely distributed kiwifruit flowering period detection task. The research results provide a high-precision and lightweight detection method reference for kiwifruit intelligent pollination robot, and have practical application value for improving the level of orchard automation management.