Video based deep learning deciphers honeybee waggle dances in natural conditions
Urbanization and industrial agriculture are a threat to wild and managed honey-bees, crucial pollinators of the natural- and agro-ecosystems components of the landscapes. Understanding bee colonies’ foraging behaviors within these landscapes is essential for managing human-bee conflicts and sustaini...
| Autores principales: | , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/177838 |
| _version_ | 1855535126810722304 |
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| author | Grison, Sylvain Siddaganga, Rajath Hedge, Shrihari Burridge, James Blok, Pieter M. Krishnan, Smitha Brockmann, Axel Guo, Wei |
| author_browse | Blok, Pieter M. Brockmann, Axel Burridge, James Grison, Sylvain Guo, Wei Hedge, Shrihari Krishnan, Smitha Siddaganga, Rajath |
| author_facet | Grison, Sylvain Siddaganga, Rajath Hedge, Shrihari Burridge, James Blok, Pieter M. Krishnan, Smitha Brockmann, Axel Guo, Wei |
| author_sort | Grison, Sylvain |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Urbanization and industrial agriculture are a threat to wild and managed honey-bees, crucial pollinators of the natural- and agro-ecosystems components of the landscapes. Understanding bee colonies’ foraging behaviors within these landscapes is essential for managing human-bee conflicts and sustaining their vital pollination services. Objectives To understand how bees use their surroundings, researchers often decode bee waggle dances, a behavior that communicates navigational information about desirable foraging sites to their nest mates. This process is carried out manually, which is time-consuming, prone to human error and requires specialized skills. We aim at developing an automatic pipeline to detect and translate waggle dances in natural conditions. Methods We introduce a novel deep learning-based pipeline that automatically detects and measures waggle runs, the core movement of the waggle dance, under natural recording conditions for the first time. With this information we can estimate the spatial and temporal dynamics of bee foraging behavior. Results Comparison of our pipeline with analysis made by human experts revealed that our procedure is able to detect 100% of waggle runs on the testing dataset, with a run duration Root Mean Squared Error (RMSE) of less than a second, and a run angle RMSE of 0.21 radians. It is also generalizable to other recording conditions and bee species. Conclusion Our approach enables precise measurement of direction and duration, enabling the spatial and temporal analysis of bee foraging behavior on an unprecedented scale compared to traditional manual methods, contributing to preserving biodiversity and ecosystem services. |
| format | Journal Article |
| id | CGSpace177838 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1778382025-12-08T10:29:22Z Video based deep learning deciphers honeybee waggle dances in natural conditions Grison, Sylvain Siddaganga, Rajath Hedge, Shrihari Burridge, James Blok, Pieter M. Krishnan, Smitha Brockmann, Axel Guo, Wei machine learning pollination artificial intelligence landscape bees foraging Urbanization and industrial agriculture are a threat to wild and managed honey-bees, crucial pollinators of the natural- and agro-ecosystems components of the landscapes. Understanding bee colonies’ foraging behaviors within these landscapes is essential for managing human-bee conflicts and sustaining their vital pollination services. Objectives To understand how bees use their surroundings, researchers often decode bee waggle dances, a behavior that communicates navigational information about desirable foraging sites to their nest mates. This process is carried out manually, which is time-consuming, prone to human error and requires specialized skills. We aim at developing an automatic pipeline to detect and translate waggle dances in natural conditions. Methods We introduce a novel deep learning-based pipeline that automatically detects and measures waggle runs, the core movement of the waggle dance, under natural recording conditions for the first time. With this information we can estimate the spatial and temporal dynamics of bee foraging behavior. Results Comparison of our pipeline with analysis made by human experts revealed that our procedure is able to detect 100% of waggle runs on the testing dataset, with a run duration Root Mean Squared Error (RMSE) of less than a second, and a run angle RMSE of 0.21 radians. It is also generalizable to other recording conditions and bee species. Conclusion Our approach enables precise measurement of direction and duration, enabling the spatial and temporal analysis of bee foraging behavior on an unprecedented scale compared to traditional manual methods, contributing to preserving biodiversity and ecosystem services. 2025-11-06 2025-11-12T10:33:40Z 2025-11-12T10:33:40Z Journal Article https://hdl.handle.net/10568/177838 en Open Access application/pdf Springer Grison, S.; Siddaganga, R.; Hedge, S.; Burridge, J.; Blok, P.M.; Krishnan, S.; Brockmann, A.; Guo, W. (2025) Video based deep learning deciphers honeybee waggle dances in natural conditions. Landscape Ecology 40(11): 211. ISSN: 0921-2973 |
| spellingShingle | machine learning pollination artificial intelligence landscape bees foraging Grison, Sylvain Siddaganga, Rajath Hedge, Shrihari Burridge, James Blok, Pieter M. Krishnan, Smitha Brockmann, Axel Guo, Wei Video based deep learning deciphers honeybee waggle dances in natural conditions |
| title | Video based deep learning deciphers honeybee waggle dances in natural conditions |
| title_full | Video based deep learning deciphers honeybee waggle dances in natural conditions |
| title_fullStr | Video based deep learning deciphers honeybee waggle dances in natural conditions |
| title_full_unstemmed | Video based deep learning deciphers honeybee waggle dances in natural conditions |
| title_short | Video based deep learning deciphers honeybee waggle dances in natural conditions |
| title_sort | video based deep learning deciphers honeybee waggle dances in natural conditions |
| topic | machine learning pollination artificial intelligence landscape bees foraging |
| url | https://hdl.handle.net/10568/177838 |
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