首页|University of Tsukuba Researchers Publish New Studies and Find- ings in the Area of Robotics (Intrarow Uncut Weed Detection Using You-Only-Look-Once Instance Segmentation for Orchard Planta- tions)
University of Tsukuba Researchers Publish New Studies and Find- ings in the Area of Robotics (Intrarow Uncut Weed Detection Using You-Only-Look-Once Instance Segmentation for Orchard Planta- tions)
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Investigators publish new report on robotics. According to news reporting from Tsukuba, Japan, by NewsRx journalists, research stated, "Mechanical weed management is a drudging task that requires manpower and has risks when conducted within rows of orchards. However, intrarow weeding must still be conducted by manual labor due to the restricted movements of riding mowers within the rows of orchards due to their confined structures with nets and poles." The news editors obtained a quote from the research from University of Tsukuba: "However, au- tonomous robotic weeders still face challenges identifying uncut weeds due to the obstruction of Global Navigation Satellite System (GNSS) signals caused by poles and tree canopies. A properly designed intel- ligent vision system would have the potential to achieve the desired outcome by utilizing an autonomous weeder to perform operations in uncut sections. Therefore, the objective of this study is to develop a vision module using a custom-trained dataset on YOLO instance segmentation algorithms to support autonomous robotic weeders in recognizing uncut weeds and obstacles (i.e., fruit tree trunks, fixed poles) within rows. The training dataset was acquired from a pear orchard located at the Tsukuba Plant Innovation Research Center (T-PIRC) at the University of Tsukuba, Japan. In total, 5000 images were preprocessed and labeled for training and testing using YOLO models. Four versions of edge-device-dedicated YOLO instance seg- mentation were utilized in this research-YOLOv5n-seg, YOLOv5s-seg, YOLOv8n-seg, and YOLOv8s-seg-for real-time application with an autonomous weeder. A comparison study was conducted to evaluate all YOLO models in terms of detection accuracy, model complexity, and inference speed. The smaller YOLOv5-based and YOLOv8-based models were found to be more efficient than the larger models, and YOLOv8n-seg was selected as the vision module for the autonomous weeder. In the evaluation process, YOLOv8n-seg had better segmentation accuracy than YOLOv5n-seg, while the latter had the fastest inference time."
University of TsukubaTsukubaJapanAsiaAutonomous RobotEmerging TechnologiesMachine LearningRoboticsRobots
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