To address the limitations inherent to conventional automated harvesting robots specifically their suboptimal success rates and risk of crop damage, we design a novel bot named AHPPEBot which is capable of autonomous harvesting based on crop phenotyping and pose estimation. Specifically, In phenotyping, the detection, association, and maturity estimation of tomato trusses and individual fruits are accomplished through a multi-task YOLOv5 model coupled with a detection-based adaptive DBScan clustering algorithm. In pose estimation, we employ a deep learning model to predict seven semantic keypoints on the pedicel. These keypoints assist in the robot's path planning, minimize target contact, and facilitate the use of our specialized end effector for harvesting. In autonomous tomato harvesting experiments conducted in commercial greenhouses, our proposed robot achieved a harvesting success rate of 86.67%, with an average successful harvest time of 32.46 s, showcasing its continuous and robust harvesting capabilities. The result underscores the potential of harvesting robots to bridge the labor gap in agriculture.

翻译：为解决传统自动化采摘机器人成功率低且易损伤作物的固有局限，我们设计了一款名为AHPPEBot的新型机器人，其能够基于作物表型与姿态估计实现自主采摘。具体而言，在表型分析中，通过多任务YOLOv5模型结合基于检测的自适应DBScan聚类算法，实现了番茄果串与单果的检测、关联及成熟度估计。在姿态估计中，我们采用深度学习模型预测花梗上的七个语义关键点。这些关键点辅助机器人进行路径规划，最大限度减少与目标物的接触，并配合专用末端执行器完成采摘。在商业温室开展的番茄自主采摘实验中，所提机器人实现了86.67%的采摘成功率，平均单次成功采摘时间为32.46秒，展现出连续稳健的采摘能力。该成果彰显了采摘机器人弥补农业劳动力缺口的发展潜力。