提高葡萄藤节点检测以支持修剪自动化:利用最先进的YOLO检测模型进行二维图像分析
Francisco Oliveira1,2, Daniel Queirós da Silva2, Vítor Filipe1,2
1School of Science and Technology, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
这项研究引入了一种新的方法,用于使用先进的YOLO模型检测葡萄树上的节点,这对于自动化机器人修剪至关重要. YOLOv7显示了对葡萄园实时节点检测的精度和速度的最佳平衡.
科学领域:
- 农业机器人农业机器人
- 计算机视觉 计算机视觉
- 葡萄种植技术 葡萄种植技术
背景情况:
- 自动修剪需要精确识别葡萄树上的切割点.
- 葡萄树木上的节点检测对于确定最佳的修剪位置至关重要.
- 目前的方法面临着机器人操纵和环境感知方面的挑战.
研究的目的:
- 提出和评估一种使用最先进的YOLO模型检测葡萄藤节点的新方法.
- 评估YOLOv7,YOLOv8,YOLOv9和YOLOv10在不同葡萄园条件下的强度和性能.
- 创建一个公开可用的数据集,用于葡萄藤节点检测.
主要方法:
- 在葡萄树图像上的YOLOv7,YOLOv8,YOLOv9和YOLOv10模型的培训和验证.
- 使用公共数据集,使用人工背景和来自葡萄牙地区的真实葡萄园图像.
- 基于准确性 (F1-Score) 和推断速度评估模型性能.
主要成果:
- 所有经过测试的YOLO模型都成功地检测到了各种葡萄树数据集中的节点.
- YOLOv7显示了准确度 (70%-86.5%F1-Score) 和推断速度 (约. 89 ms) 的时间.
- 该研究产生了一个新的,公开可访问的葡萄牙葡萄园图像数据集,用于节点检测.
结论:
- 提出的基于YOLO的节点检测方法对于实时应用程序是有效的.
- 由于其性能平衡,YOLOv7被确定为自主机器人修剪系统最适合的模型.
- 这项研究有助于在葡萄栽培中开发自动化机器人修剪系统.
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