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    ARTICLE

    An Improved YOLOv8-Based Method for Real-Time Detection of Harmful Tea Leaves in Complex Backgrounds

    Xin Leng#, Jiakai Chen#, Jianping Huang*, Lei Zhang, Zongxuan Li

    Phyton-International Journal of Experimental Botany, Vol.93, No.11, pp. 2963-2981, 2024, DOI:10.32604/phyton.2024.057166 - 30 November 2024

    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, 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. Utilizing robots for pruning can significantly enhance efficiency and reduce costs. However, the accuracy of object detection directly impacts the overall efficiency of pruning robots. In complex tea plantation environments, complex image backgrounds, the… More >

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