In recent years, the potential of robotic harvesting in greenhouse tomato production has garnered significant attention within the tomato industry. However, there is a lack of sufficient research on the complete replacement of manual harvesting with this technology. In this paper, we propose a tomato harvesting method that utilizes a nesting approach to simplify the process and minimize damage. The paper describes the tomato harvesting robot prototype, the visual system equipped with three vision-based tomato detectors: YOLOv5_CBAM, which incorporates a convolutional block attention module; YOLOv5_SE, enhanced with a squeeze-and-excitation block; and a standard YOLOv5s model. Additionally, a novel shear gripping method for fruit bunches is presented, utilizing a bottom-up snapping technique during harvesting. Point cloud data are utilized to determine the position of the tomato's main stem and bunch. The paper includes field tests and experimental findings, which indicate that the YOLOv5_CBAM model achieves the highest precision (82.62%) and recall (82.57%), outperforming YOLOv5_SE and standard YOLOv5s. Field experiments demonstrate that the improved end-effector and vision system have significantly enhanced the robot's performance, achieving a 57.5% harvesting success rate in just 14.9 s.

中文翻译：

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番茄采摘机器人果实生长方向识别及嵌套抓取策略

近年来，机器人收获在温室番茄生产中的潜力引起了番茄行业的高度关注。然而，对于用该技术完全替代人工采收还缺乏足够的研究。在本文中，我们提出了一种番茄收获方法，该方法利用嵌套方法来简化过程并最大程度地减少损失。论文描述了番茄采摘机器人原型，该视觉系统配备了三个基于视觉的番茄检测器：YOLOv5_CBAM，它集成了卷积块注意力模块； YOLOv5_SE，通过挤压和激励模块进行增强；和标准 YOLOv5s 模型。此外，还提出了一种新颖的果串剪切夹持方法，在收获过程中利用自下而上的咬合技术。利用点云数据来确定番茄主茎和番茄串的位置。论文包括现场测试和实验结果，表明YOLOv5_CBAM模型实现了最高的精度（82.62％）和召回率（82.57％），优于YOLOv5_SE和标准YOLOv5。现场实验表明，改进的末端执行器和视觉系统显着提高了机器人的性能，仅在 14.9 秒内就实现了 57.5% 的收获成功率。