Research papers from 2025

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