Inspection photos, entrance videos, metrics, tracks, and external reference resources for experiments and benchmarking.
Research library
Research
Papers, datasets, models, and field notes are connected into a practical research map for digital beekeeping, from academic references to applied Gratheon experiments.
Gratheon research assets
Bee detection, queen detection, BeePose, and varroa-on-bee detection spanning browser inference, HTTP services, and edge experimentation.
A direct technical entry point into Gratheon’s own monitoring work and collaboration discussions.
Research map
Navigate the research collection
Gratheon Research
Gratheon Research is a working knowledge base for digital beekeeping: academic literature, field datasets, machine learning models, and engineering experiments connected in one place.
We focus on applied research in bee observability, colony health, computer vision, IoT sensing, and automation. These threads inform ongoing work on Entrance Observer, the robotic beehive, and AI-assisted analysis in the web app.
112
paper notes tracked
2009–2026
publication range
55
computer vision papers
44
IoT & sensor papers
Research focus areas
Computer vision
Entrance traffic analysis, pollen and hornet detection, comb inspection, pose estimation, and behavior recognition for real-world apiary conditions.
Varroa and colony health
Varroa imaging, queenlessness signals, brood and comb analysis, pollination outcomes, and practical health observability.
IoT and observability
Hive scales, weather context, acoustic sensing, remote telemetry, power constraints, and edge-device deployments.
Robotics and automation
Robotic comb mapping, biomimetic systems, and autonomous-apiary experiments linked to field monitoring.
Surveys and reviews
Landscape overviews of precision beekeeping, digital monitoring stacks, and machine learning methods.
Research papers index
Browse the full bibliography by year, topic, product area, and research team without changing existing paper URLs.
Highlighted papers and threads
COMB: Common Open Modular robotic platform for Bees
Open modular robotics work from the University of Konstanz and Freie Universität Berlin, relevant to future autonomous-apiary experiments.
Fanning behavior detection at the hive entrance
Object-detection benchmarking for behavior signals that can strengthen entrance monitoring and colony-state interpretation.
Open-source varroa mite fall analysis
Practical vision tooling for colony-health workflows and varroa-related evaluation.
Continuous electronic beehive monitoring datasets
Reference data spanning audio, image, video, and weather signals for continuous hive observability.
Research collaborations
University of Tartu student teams
Applied AI work on bee type detection, hornet detection, and multi-class entrance monitoring.
TalTech / VIDRIK collaboration
Applied research and engineering exchange, including a joint white paper preprint.
Bachelor thesis support
Support for academic work on digital beekeeping solutions, practical system design, and field-ready monitoring workflows.
Open to collaboration
We welcome researchers, universities, and applied engineering teams working on bee health, observability, machine learning, and responsible automation.