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In unstructured environments, the cluster growth characteristics of Hangzhou white chrysanthemum lead to severe mutual occlusion, reducing detection accuracy for chrysanthemum detection algorithms. To address this issue, an improved YOLO v8n detection model for Hangzhou white chrysanthemum, called Hangzhou white chrysanthemum-YOLO v8n (Hwc-YOLO v8n), was proposed. Firstly, the model’s ability was enhanced to finely detect critical, similar features of the chrysanthemum by increasing the label categories from two to three. Secondly, a dynamic feature extraction module (C2f-Dynamic) was designed in the backbone network to strengthen the model’s adaptive response to missing features in occluded targets. Additionally, a 160 pixel×160 pixel detection head was added to the detection head section, allowing the model to detect small targets more effectively. Finally, the angle penalty metric loss (SIoU) was adopted to optimize the bounding box loss function, improving both detection accuracy and generalization capability. Experimental results from module placement and heatmap analysis demonstrated that the C2f-Dynamic module can dynamically adapt to feature changes in occluded targets. The improved Hwc-YOLO v8n model achieved a 1.7 percentage points increase in mean average precision and a 0.88 percentage points increase in mean recall rate for the occluded Hangzhou white chrysanthemum. Ablation and comparison experiments showed that the improved Hwc-YOLO v8n outperformed DETR, SSD, YOLO v5, YOLO v6, and YOLO v7 in detection of the chrysanthemum. Specifically, compared with DETR, SSD, YOLO v5, YOLO v6, and YOLO v7, the mAP was improved by 5.7, 12.6, 0.7, 0.75, and 11.25 percentage points, respectively. The mR was increased by 2.15 percentage points and 1.4 percentage points compared with that of YOLO v5 and YOLO v7, respectively. The research result can provide a technical foundation for future intelligent harvesting of Hangzhou white chrysanthemum.