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A Tea Buds Counting Method Based on YOLOv5 and Kalman Filter Tracking Algorithm

文献类型： 外文期刊

作者： Li, Yang 1 ; Ma, Rong 2 ; Zhang, Rentian 1 ; Cheng, Yifan 1 ; Dong, Chunwang 1 ;

作者机构： 1.Chinese Acad Agr Sci, Tea Res Inst, Key Lab Tea Qual & Safety Control, Minist Agr & Rural Affairs, Hangzhou, Peoples R China

2.Zhejiang A&F Univ, Coll Opt Mech & Elect Engn, Hangzhou, Peoples R China

3.Shandong Acad Agr Sci, Tea Res Inst, Jinan 250100, Peoples R China

4.Shihezi Univ, Coll Mech & Elect Engn, Shihezi, Peoples R China

期刊名称：PLANT PHENOMICS （ 影响因子：6.5； 五年影响因子：7.5 ）

ISSN： 2643-6515

年卷期： 2023 年 2023 卷

页码：

收录情况： SCI

摘要： The tea yield estimation provides information support for the harvest time and amount and serves as a decision-making basis for farmer management and picking. However, the manual counting of tea buds is troublesome and inefficient. To improve the efficiency of tea yield estimation, this study presents a deep-learning-based approach for efficiently estimating tea yield by counting tea buds in the field using an enhanced YOLOv5 model with the Squeeze and Excitation Network. This method combines the Hungarian matching and Kalman filtering algorithms to achieve accurate and reliable tea bud counting. The effectiveness of the proposed model was demonstrated by its mean average precision of 91.88% on the test dataset, indicating that it is highly accurate at detecting tea buds. The model application to the tea bud counting trials reveals that the counting results from test videos are highly correlated with the manual counting results (R2 = 0.98), indicating that the counting method has high accuracy and effectiveness. In conclusion, the proposed method can realize tea bud detection and counting in natural light and provides data and technical support for rapid tea bud acquisition.

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