Research on daylily buds recognition based on an improved YOLOv8 model

doi:10.14088/j.cnki.issn0439-8114.2025.07.032

Abstract

Abstract: A CSPDenseNet backbone module was constructed by deeply integrating CSPNet and DenseNet. This module was integrated into the YOLOv8 model, replacing the last two standard convolutional modules at the end of the backbone network, resulting in the improved YOLOv8 model (Dense-YOLOv8).The results demonstrated that the Dense-YOLOv8 model successfully identified all mature buds under scenarios with a simple background and sparse daylily (Hemerocallis citrina) Baroni buds. Under scenarios with a simple background and dense daylily buds, the Dense-YOLOv8 model exhibited excellent recognition performance in the bud detection task, although some missed detections still occurred when processing tightly adjacent targets.Under scenarios with a complex background and dense daylily buds, the Dense-YOLOv8 model successfully identified all mature buds.The mAP, F1, recognition speed, and model size of the Dense-YOLOv8 model were 90.75%, 89%, 53 f/s, and 217.68 MB, respectively. Compared with the YOLOv8 model, Faster R-CNN model and YOLOv7 model, the Dense-YOLOv8 model significantly improved both the accuracy and speed of object detection while streamlining the network structure and reducing parameters.

Key words: improved YOLOv8 model, deep learning, daylily (Hemerocallis citrina Baroni), bud, recognition

CLC Number:

TP391

HUO Jing-qi, CUI Ting-ting, XUE Zhi-lu. Research on daylily buds recognition based on an improved YOLOv8 model[J]. HUBEI AGRICULTURAL SCIENCES, 2025, 64(7): 186-191.

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