ArticleScientific reports2017
Automated computer-based detection of encounter behaviours in groups of honeybees.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 34 citations in OpenAlex.
- The fru gene specifies male cooperative behaviors in honeybee colonies.Nature communications · 2025Article
- Automated entrance monitoring to investigate honey bee foraging trips using open-source wireless platform and fiducial tags.HardwareX · 2024Article
- Dedicated developmental programing for group-supporting behaviors in eusocial honeybees.Science advances · 2024Article
- Automated monitoring of honey bees with barcodes and artificial intelligence reveals two distinct social networks from a single affiliative behavior.Scientific reports · 2023Article
- Decoding alarm signal propagation of seed-harvester ants using automated movement tracking and supervised machine learning.Proceedings. Biological sciences · 2022Article
- Semi-automatic detection of honeybee brood hygiene-an example of artificial learning to facilitate ethological studies on social insects.Biology methods & protocols · 2022Article
- Three-Dimensional Tracking of Multiple Small Insects by a Single Camera.Journal of insect science (Online) · 2021Article
- Markerless tracking of an entire honey bee colony.Nature communications · 2021Article
- Social networks predict the life and death of honey bees.Nature communications · 2021Article
- Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence.Frontiers in robotics and AI · 2018Article
- RenderGAN: Generating Realistic Labeled Data.Frontiers in robotics and AI · 2018Article
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Corrections and comments
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Authors and funding
9 authors at 5 institutions in 3 countries.
Funding
Abstract
Honeybees form societies in which thousands of members integrate their behaviours to act as a single functional unit. We have little knowledge on how the collaborative features are regulated by workers' activities because we lack methods that enable collection of simultaneous and continuous behavioural information for each worker bee. In this study, we introduce the Bee Behavioral Annotation System (BBAS), which enables the automated detection of bees' behaviours in small observation hives. Continuous information on position and orientation were obtained by marking worker bees with 2D barcodes in a small observation hive. We computed behavioural and social features from the tracking information to train a behaviour classifier for encounter behaviours (interaction of workers via antennation) using a machine learning-based system. The classifier correctly detected 93% of the encounter behaviours in a group of bees, whereas 13% of the falsely classified behaviours were unrelated to encounter behaviours. The possibility of building accurate classifiers for automatically annotating behaviours may allow for the examination of individual behaviours of worker bees in the social environments of small observation hives. We envisage that BBAS will be a powerful tool for detecting the effects of experimental manipulation of social attributes and sub-lethal effects of pesticides on behaviour.
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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.