Background The declining availability of natural pollinators and limitations of contact-based robotic pollination methods including flower damage, pathogen transmission, and reduced operational efficiency necessitate innovative solutions for protected horticulture. Gap Existing robotic pollinators achieve limited success rates (∼ 66%) primarily due to inaccurate 6D flower pose estimation, while current airflow-based systems lack precise positioning capabilities. Contribution This study presents a novel YOLOv8-PnP hybrid framework integrating real-time object detection with 6 degree of freedom pose estimation for precision airflow based pollination. The system employs a custom-designed Air Pollenmatic end-effector integrated with a Hello Robot Stretch platform through ROS-based visual servoing control. Results Validation on 2,100 annotated greenhouse images demonstrated 95.8% precision, 94.6% recall, and 97.7% mAP@0.5 at 28.5 FPS (11.1 ms inference). Field trials achieved 92.5% pollination attempt rate and 85.6% success rate, yielding 79.2% overall efficacy—an 8.3 percentage point improvement over contact-based methods. Impact This contactless approach eliminates mechanical flower damage, reduces disease transmission risk, and advances the feasibility of fully autonomous greenhouse pollination systems.
Singh et al. (Thu,) studied this question.