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Potted Phalaenopsis Grading: Precise Bloom and Bud Counting with the PA-YOLO Algorithm and Multiviewpoint Imaging

文献类型： 外文期刊

作者： Yang, Yi 1 ; Zhang, Guankang 1 ; Ma, Shutao 1 ; Wang, Zaihua 2 ; Liu, Houcheng 4 ; Gu, Song 5 ; Marani, Roberto 1 ;

作者机构： 1.South China Agr Univ, Coll Elect Engn, Guangzhou 510642, Peoples R China

2.Guangdong Acad Agr Sci, Environm Hort Res Inst, Guangzhou 510640, Peoples R China

3.Guangdong Prov Key Lab Ornamental Plant Germplasm, Guangzhou 510640, Peoples R China

4.South China Agr Univ, Coll Hort, Guangzhou 510642, Peoples R China

5.South China Agr Univ, Coll Engn, Guangzhou 510642, Peoples R China

6.South China Agr Univ, Key Lab Key Technol Agr Machine & Equipment, Minist Educ, Guangzhou 510642, Peoples R China

关键词： flower counting; deep learning; multiple viewpoints; flower detection; potted Phalaenopsis; machine vision

期刊名称：AGRONOMY-BASEL （ 影响因子：3.7； 五年影响因子：4.0 ）

ISSN：

年卷期： 2024 年 14 卷 1 期

页码：

收录情况： SCI

摘要： The accurate detection and counting of flowers ensure the grading quality of the ornamental plants. In automated potted flower grading scenarios, low detection precision, occlusions and overlaps impact counting accuracy. This study proposed a counting method combining a deep learning algorithm with multiple viewpoints. Firstly, a flower detection model, PA-YOLO, was developed based on YOLOv5 by designing a two-scale detection branch, optimizing the number of bottlenecks and integrating a dynamic head framework. Next, PA-YOLO was used to detect grouped 360-viewpoint images of each potted plant to determine the optimal number of viewpoints for counting. The detection results indicated that PA-YOLO achieved a mean average precision (mAP) of 95.4% and an average precision (AP) of 91.9% for occluded blooms on our Phalaenopsis flower dataset. For the optimal number of viewpoints, the average counting accuracy of buds and blooms was highest at three viewpoints, with scores of 96.25% and 93.33%, respectively. The final counting accuracy reached 95.56% in flower counting tests conducted from three viewpoints. The overall results suggest that the proposed method can effectively detect and count flowers in complex occlusion and overlap environments, providing guidance for designing and implementing the vision component in an automated potted flower grading system.

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