An Improved YOLOv8-based Method for Real-time Detection of Harmful Tea Leaves in Complex Backgrounds

Abstract

Abstract Tea, a globally cultivated crop renowned for its unique flavor profile and health-promoting properties, ranks among the most favored functional beverages worldwide. However, pests and diseases severely jeopardize the production and quality of tea leaves, leading to significant economic losses.While early and accurate identification coupled with the removal of infected leaves can mitigate widespread infection, manual leaves removal remains time-consuming and expensive. To address this challenge, this paper introduces the YOLO-DBD network model for detecting of harmful tea leaves. The model excels in efficiently identifying harmful tea leaves with various poses in complex backgrounds, providing crucial guidance for the posture and obstacle avoidance of a robotic arm during the pruning process.The improvements proposed in this study encompass the C2f-DCN module, Bi-Level Routing Attention, Dynamic Head, and Focal-CIoU Loss function, enhancing the model's feature extraction, computation allocation, and perception capabilities. Comparative analysis with the YOLOv8s model demonstrates a 6% improvement in mAP and a reduction of 3.3G FLOPs in the YOLO-DBD model.

Plant phenotyping relevance

有害茶葉の画像検出モデルを開発し、病害・障害状態の直接的な推定を行う技術が研究の中心であるため、植物表現型計測手法として含める。

Code and data availability

The supplied blocks describe a self-collected dataset of 1,108 harmful tea leaf images (IHL/CHL/OHL) and the YOLO-DBD model, but contain no data availability statement, no public dataset deposit, and no author code release with a public URL. The only URLs present are citations to prior work, which do not qualify.

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