Summary
- Year: 2025
- Total papers: 20
- Research papers hub
- All years
Topics
- Audio Acoustics (5)
- Bee Behaviour (2)
- Colony Health (2)
- Computer Vision (9)
- Datasets (1)
- Datasets Benchmarks (4)
- Edge AI Energy (5)
- IoT Sensors (10)
- Pollination Ecology (2)
- Precision Beekeeping (1)
- Reviews Surveys (4)
- Varroa Health (3)
Product areas
- Colony Health (7)
- Edge Device (1)
- Gate Tracker (3)
- Hive Scanner (4)
- Monitoring Platform (11)
Papers by topic
Audio Acoustics
- buzzdetect: an open-source deep learning tool for automated bioacoustic pollinator monitoring — 🇺🇸 The Ohio State University; 🇺🇸 Dartmouth College
- Deep Edge IoT for Acoustic Detection of Queenless Beehives — 🇬🇷 Aristotle University of Thessaloniki
- Spectral Components of Honey Bee Sound Signals Recorded Inside and Outside the Beehive: An Explainable Machine Learning Approach to Diurnal Pattern Recognition — 🇵🇱 Wrocław University of Science and Technology; 🇵🇱 AGH University of Krakow
- Transformer Models improve the acoustic recognition of buzz-pollinating bee species — 🇧🇷 Universidade Federal de Goiás; 🇺🇸 University of Arizona; 🇨🇱 Universidad Católica del Maule
- UrBAN: Urban Beehive Acoustics and PheNotyping Dataset — 🇨🇦 Institut National de la Recherche Scientifique; 🇨🇦 Université Laval; 🇨🇦 Nectar Technologies Inc.
Bee Behaviour
- Evaluation of Single-Shot Object Detection Models for Identifying Fanning Behavior in Honeybees at the Hive Entrance — 🇱🇹 Vilnius Gediminas Technical University
- Visual recognition of honeybee behavior patterns at the hive entrance — 🇱🇹 Vilnius Gediminas Technical University
Colony Health
- Image-based honey bee larval viral and bacterial diagnosis using machine learning — 🇺🇸 USDA Carl Hayden Bee Research Center; 🇺🇸 University of Arizona; 🇺🇸 Arizona Science Center
- Queen Detection in Beehives via Environmental Sensor Fusion for Low-Power Edge Computing — 🇨🇭 Institute of Neuroinformatics, University of Zurich and ETH Zurich; 🇨🇭 Digital Society Initiative, University of Zurich
Computer Vision
- An AI-Based Digital Scanner for Varroa destructor Detection in Beekeeping — 🇮🇹 Council for Agricultural Research and Economics (CREA) – Research Centre for Engineering and Agro-Food Processing, Monterotondo; 🇮🇹 Council for Agricultural Research and Economics (CREA) – Research Centre for Agriculture and Environment, Bologna; 🇷🇸 University of Novi Sad
- An AI-Based Open-Source Software for Varroa Mite Fall Analysis in Honeybee Colonies — 🇪🇸 University of Zaragoza; 🇪🇸 University of La Rioja; 🇪🇸 University of Valencia
- Apis mellifera Bee Verification with IoT and Graph Neural Network — 🇲🇽 Instituto Tecnológico El Llano Aguascalientes
- Deep Learning-Based Detection of Honey Storage Areas in Apismellifera Colonies for Predicting Physical Parameters of Honey via Linear Regression — 🇹🇭 Chiang Mai University
- Evaluation of Single-Shot Object Detection Models for Identifying Fanning Behavior in Honeybees at the Hive Entrance — 🇱🇹 Vilnius Gediminas Technical University
- Fast, accurate measurement of the worker populations of honey bee colonies using deep learning — 🇺🇸 Arizona State University; 🇺🇸 Texas A&M University–Kingsville
- Image-based honey bee larval viral and bacterial diagnosis using machine learning — 🇺🇸 USDA Carl Hayden Bee Research Center; 🇺🇸 University of Arizona; 🇺🇸 Arizona Science Center
- Towards Varroa destructor mite detection using a narrow spectra illumination — 🇨🇿 Brno University of Technology
- Visual recognition of honeybee behavior patterns at the hive entrance — 🇱🇹 Vilnius Gediminas Technical University
Datasets
- Visual recognition of honeybee behavior patterns at the hive entrance — 🇱🇹 Vilnius Gediminas Technical University
Datasets Benchmarks
- buzzdetect: an open-source deep learning tool for automated bioacoustic pollinator monitoring — 🇺🇸 The Ohio State University; 🇺🇸 Dartmouth College
- Fast, accurate measurement of the worker populations of honey bee colonies using deep learning — 🇺🇸 Arizona State University; 🇺🇸 Texas A&M University–Kingsville
- Image-based honey bee larval viral and bacterial diagnosis using machine learning — 🇺🇸 USDA Carl Hayden Bee Research Center; 🇺🇸 University of Arizona; 🇺🇸 Arizona Science Center
- UrBAN: Urban Beehive Acoustics and PheNotyping Dataset — 🇨🇦 Institut National de la Recherche Scientifique; 🇨🇦 Université Laval; 🇨🇦 Nectar Technologies Inc.
Edge AI Energy
- A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management — 🇦🇺 University of Technology Sydney; 🇦🇺 BeeSTAR
- Deep Edge IoT for Acoustic Detection of Queenless Beehives — 🇬🇷 Aristotle University of Thessaloniki
- Queen Detection in Beehives via Environmental Sensor Fusion for Low-Power Edge Computing — 🇨🇭 Institute of Neuroinformatics, University of Zurich and ETH Zurich; 🇨🇭 Digital Society Initiative, University of Zurich
- Spectral Components of Honey Bee Sound Signals Recorded Inside and Outside the Beehive: An Explainable Machine Learning Approach to Diurnal Pattern Recognition — 🇵🇱 Wrocław University of Science and Technology; 🇵🇱 AGH University of Krakow
- WaggleNet: A LoRa and MQTT-Based Monitoring System for Internal and External Beehive Conditions — 🇰🇷 Kyonggi University; 🇰🇷 Hallym University; 🇰🇷 Jeonbuk National University; 🇺🇸 Purdue University
IoT Sensors
- A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management — 🇦🇺 University of Technology Sydney; 🇦🇺 BeeSTAR
- Apis mellifera Bee Verification with IoT and Graph Neural Network — 🇲🇽 Instituto Tecnológico El Llano Aguascalientes
- Beekeeping in the digital age: prospects and pitfalls of hive sensors in commercial beekeeping — 🇦🇺 University of Melbourne; 🇦🇺 La Trobe University; 🇦🇺 ANU Research School of Biology
- Buzzing with Intelligence: A Systematic Review of Smart Beehive Technologies — 🇭🇷 University of Split
- Deep Edge IoT for Acoustic Detection of Queenless Beehives — 🇬🇷 Aristotle University of Thessaloniki
- IoT and Machine Learning Techniques for Precision Beekeeping:A Review — 🇺🇬 Makerere University; 🇹🇿 Dar es Salaam Institute of Technology
- Queen Detection in Beehives via Environmental Sensor Fusion for Low-Power Edge Computing — 🇨🇭 Institute of Neuroinformatics, University of Zurich and ETH Zurich; 🇨🇭 Digital Society Initiative, University of Zurich
- Spectral Components of Honey Bee Sound Signals Recorded Inside and Outside the Beehive: An Explainable Machine Learning Approach to Diurnal Pattern Recognition — 🇵🇱 Wrocław University of Science and Technology; 🇵🇱 AGH University of Krakow
- UrBAN: Urban Beehive Acoustics and PheNotyping Dataset — 🇨🇦 Institut National de la Recherche Scientifique; 🇨🇦 Université Laval; 🇨🇦 Nectar Technologies Inc.
- WaggleNet: A LoRa and MQTT-Based Monitoring System for Internal and External Beehive Conditions — 🇰🇷 Kyonggi University; 🇰🇷 Hallym University; 🇰🇷 Jeonbuk National University; 🇺🇸 Purdue University
Pollination Ecology
- buzzdetect: an open-source deep learning tool for automated bioacoustic pollinator monitoring — 🇺🇸 The Ohio State University; 🇺🇸 Dartmouth College
- Transformer Models improve the acoustic recognition of buzz-pollinating bee species — 🇧🇷 Universidade Federal de Goiás; 🇺🇸 University of Arizona; 🇨🇱 Universidad Católica del Maule
Precision Beekeeping
- Beekeeping in the digital age: prospects and pitfalls of hive sensors in commercial beekeeping — 🇦🇺 University of Melbourne; 🇦🇺 La Trobe University; 🇦🇺 ANU Research School of Biology
Reviews Surveys
- A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management — 🇦🇺 University of Technology Sydney; 🇦🇺 BeeSTAR
- Beekeeping in the digital age: prospects and pitfalls of hive sensors in commercial beekeeping — 🇦🇺 University of Melbourne; 🇦🇺 La Trobe University; 🇦🇺 ANU Research School of Biology
- Buzzing with Intelligence: A Systematic Review of Smart Beehive Technologies — 🇭🇷 University of Split
- IoT and Machine Learning Techniques for Precision Beekeeping:A Review — 🇺🇬 Makerere University; 🇹🇿 Dar es Salaam Institute of Technology
Varroa Health
- An AI-Based Digital Scanner for Varroa destructor Detection in Beekeeping — 🇮🇹 Council for Agricultural Research and Economics (CREA) – Research Centre for Engineering and Agro-Food Processing, Monterotondo; 🇮🇹 Council for Agricultural Research and Economics (CREA) – Research Centre for Agriculture and Environment, Bologna; 🇷🇸 University of Novi Sad
- An AI-Based Open-Source Software for Varroa Mite Fall Analysis in Honeybee Colonies — 🇪🇸 University of Zaragoza; 🇪🇸 University of La Rioja; 🇪🇸 University of Valencia
- Towards Varroa destructor mite detection using a narrow spectra illumination — 🇨🇿 Brno University of Technology