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

• Published in: PloS one Vol. 19; no. 12; p. e0315465
• Main Authors: Xie, Zhongjian, Zhang, Yaya, Wu, Weilin, Xiao, Yao, Chen, Xinwei, Chen, Weiqi, Wan, ZhuXuan, Lin, Chunhua
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.12.2024; Public Library of Science (PLoS)
• Subjects: 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
• DOI: 10.1371/journal.pone.0315465

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.