Hydroponic Chinese flowering cabbage detection and localization algorithm based on improved YOLOv5s

To achieve automated harvesting of hydroponic Chinese flowering cabbage, the detection and localization of the cabbage are crucial. This study proposes a two stages detection and localization algorithm for hydroponic Chinese flowering cabbage, which includes macro-detection and micro-localization. T...

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Veröffentlicht in:PloS one Jg. 19; H. 12; S. e0315465
Hauptverfasser: Xie, Zhongjian, Zhang, Yaya, Wu, Weilin, Xiao, Yao, Chen, Xinwei, Chen, Weiqi, Wan, ZhuXuan, Lin, Chunhua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 16.12.2024
Public Library of Science (PLoS)
Schlagworte:
Agricultural production
Algorithms
Analysis
Automation
Biology and Life Sciences
Brassica - growth & development
Brassica oleracea
Cameras
Chinese cabbage
Computer and Information Sciences
Coordinate transformations
Datasets
Deep learning
Flowering
Fruits
Harvesting
Hydroponics
Hydroponics - methods
Localization
Modules
Object recognition
Parameters
Physical Sciences
Production management
Receptive field
Research and Analysis Methods
Vegetables
China
ISSN:1932-6203, 1932-6203
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Zusammenfassung:To achieve automated harvesting of hydroponic Chinese flowering cabbage, the detection and localization of the cabbage are crucial. This study proposes a two stages detection and localization algorithm for hydroponic Chinese flowering cabbage, which includes macro-detection and micro-localization. The macro-detection algorithm is named P-YOLOv5s-GRNF. Its improvement strategies include adopting pruning techniques, the GSConv, receptive field attention convolution (RFAConv), normalization-based attention module (NAM), and the Focal-EIOU Loss module. The micro-localization algorithm is named YOLOv5s-SBC. Its improvement strategies include adding a 160×160 detection layer, removing a 20×20 detection layer, introducing a weighted bidirectional feature pyramid network (BiFPN) structure, and utilizing the coordinate attention (CA) mechanism. The experimental results showed that P-YOLOv5s-GRNF increased the mAP(mean average precision) by 0.8%, 4.3%, 3.2%, 0.7%, 19.3%, 9.8%, 3.1% compared to mainstream object detection algorithms YOLOv5s, YOLOv6s, YOLOv7-tiny, YOLOv8s, YOLOv5s-Shufflenetv2, YOLOv5s-Mobilenetv3, YOLOv5s-Ghost, respectively. Compared to the original model, P-YOLOv5s-GRNF decreased parameters by 18%, decreased model size to 11.9MB, decreased FLOPs to 14.5G, and increased FPS by 4.3. YOLOv5s-SBC also increased mAP by 4.0% compared to the original YOLOv5s, with parameters decreased by 65%, model size decreased by 60%, and FLOPs decreased to 15.3G. Combined with a depth camera, the improved models construct a positioning system that can provide technical support for the automated and intelligent harvesting of Chinese flowering cabbage.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0315465