ArticleFrontiers in plant science2026
YOLOv8-PnP fusion architecture for non-contact robotic pollination: a 6D pose estimation approach for autonomous greenhouse operations.
Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.