ArticleEcology and evolution2022
Bee Tracker-an open-source machine learning-based video analysis software for the assessment of nesting and foraging performance of cavity-nesting solitary bees.
Article in Ecology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Computer Vision for Monitoring Wild Bees and Wasps: A Structured Literature Review.Ecology and evolution · 2026Review
- Integrative identification of insect flower visitors associated with avocado flowering in Western Australia.Environmental entomology · 2026Article
- Increased overwintering temperature reduces reproductive success of the solitary bee species Osmia bicornis.Scientific reports · 2025Article
- A Brazilian native bee (Data in brief · 2024Article
- Emerging threats and opportunities to managed bee species in European agricultural systems: a horizon scan.Scientific reports · 2023Article
- Nutritional stress exacerbates impact of a novel insecticide on solitary bees' behaviour, reproduction and survival.Proceedings. Biological sciences · 2022Article
- Bee Tracker-an open-source machine learning-based video analysis software for the assessment of nesting and foraging performance of cavity-nesting solitary bees.Ecology and evolution · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The foraging and nesting performance of bees can provide important information on bee health and is of interest for risk and impact assessment of environmental stressors. While radiofrequency identification (RFID) technology is an efficient tool increasingly used for the collection of behavioral data in social bee species such as honeybees, behavioral studies on solitary bees still largely depend on direct observations, which is very time-consuming. Here, we present a novel automated methodological approach of individually and simultaneously tracking and analyzing foraging and nesting behavior of numerous cavity-nesting solitary bees. The approach consists of monitoring nesting units by video recording and automated analysis of videos by machine learning-based software. This
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