Reasearch on Kiwi Fruit Flower Recognition for Efficient Pollination Based on an Improved YOLOv5 Algorithm
文献类型： 外文期刊
作者： Zhou, Haili 1 ; Ou, Junlang 1 ; Meng, Penghao 1 ; Tong, Junhua 1 ; Ye, Hongbao 3 ; Li, Zhen 1 ;
作者机构： 1.Zhejiang Sci Tech Univ, Sch Mech Engn, Hangzhou 310018, Peoples R China
2.Key Lab Transplanting Equipment & Technol Zhejiang, Hangzhou 310018, Peoples R China
3.Zhejiang Acad Agr Sci, Inst Agr Equipment, Hangzhou 310021, Peoples R China
关键词： machine vision; target recognition; YOLOv5; kiwi fruit pollination
期刊名称：HORTICULTURAE （ 影响因子：3.1； 五年影响因子：3.4 ）
ISSN：
年卷期： 2023 年 9 卷 3 期
页码：
收录情况： SCI
摘要： A close relationship has been observed between the growth and development of kiwi fruit and the pollination of the kiwi flower. Flower overlap, flower tilt, and other problems will affect this plant's pollination success rate. A pollination model based on YOLOv5 was developed to improve the pollination of kiwi flowers. The K-means++ clustering method was used to cluster the anchors closer to the target size, which improved the speed of the algorithm. A convolutional block module attention mechanism was incorporated to improve the extraction accuracy with respect to kiwi flower features and effectively reduce the missed detection and error rates. The optimization of the detection function improves the recognition of flower overlap and the accuracy of flower tilt angle calculation and accurately determines flower coordinates, pollination point coordinates, and pollination angles. The experimental results show that the predicted value of the YOLOv5s model is 96.7% and that its recognition accuracy is the highest. Its mean average precision value is up to 89.1%, its F1 score ratio is 90.12%, and its memory requirements are the smallest (only 20 MB). The YOLOv5s model achieved the highest recognition accuracy as determined through a comparison experiment of the four sets of analysed models, thereby demonstrating its ability to facilitate the efficient target pollination of kiwi flowers.
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