  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.
