DeepWings: a machine learning tool for identification of honey bee subspecies

Detalhes bibliográficos
Autor(a) principal: Ariel Yadró, Carlos
Data de Publicação: 2022
Outros Autores: Rodrigues, Pedro João, Adam, Tofilski, Elen, Dylan, McCormack, Grace P., Henriques, Dora, Pinto, M. Alice
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10198/26007
Resumo: DeepWings© is a software that uses Machine Learning for fully automated identification of Apis mellifera subspecies based on wing geometric morphometrics (WGM). Here, we examined the performance of DeepWings© under realistic conditions by processing 14,782 wing images with varying quality and produced by different operators. These images represented 2,593 colonies covering the native ranges of A. m. iberiensis (Portugal, Spain and historical introduction in the Azores), A. m. mellifera (Belgium, France, Ireland, Poland, Russia, Sweden, Switzerland, UK) and A. m. carnica (Croatia, Hungary, Romania). The classification probability obtained for the colonies was contrasted with the endemic subspecies distribution. Additionally, the association between WGM classification and that inferred from microsatellites and SNPs was evaluated for 1,214 colonies. As much as 94.4% of the wings were accepted and classified by DeepWings©. In the Iberian honey bee native range, 92,6% of the colonies were classified as A. m. iberiensis with a median probability of 91.88 (IQR = 22.52). In the Azores, 85.7% of colonies were classified as A. m. iberiensis, with a median probability of 84.16 (32.40). In the Dark honey bee native range, 41.1 % of the colonies were classified as A. m mellifera with a median probability of 99.36 (8.02). The low percentage of colonies matching the native subspecies was mainly due to the low values registered in Avignon (20.0%), Poland (32.9%), and Wales (41.2%). In contrast, most of the colonies analyzed in other locations of the native range of A. m. mellifera matched this subspecies: Belgium (100.0%), Groix (63.9%), Ouessant (72.7%), Ireland (78.0%), Russia (96.2%), Sweden (84.2%) and Switzerland (55.6%). In the colonies from Croatia, Hungary, and Romania, 88.0% of the samples were classified as A. m. carnica, with a median probability of 98.49 (6.76). The association between WGM and molecular data was highly significant but not very strong (Spearman r = 0.31, p < 0.0001). A good agreement between morphological and molecular methods was registered in samples originating from highly conserved M-lineage populations whereas in populations with historical records of foreign queen importations the agreement was weaker. In general, DeepWings© showed good performance when tested under realistic conditions. It is a valuable tool that can be used not only for honey bee breeding and conservation but also for research purposes.
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spelling DeepWings: a machine learning tool for identification of honey bee subspeciesWing Geometric MorphometricsApis mellifera subspecies classificationHoney bee conservationDeepWings© is a software that uses Machine Learning for fully automated identification of Apis mellifera subspecies based on wing geometric morphometrics (WGM). Here, we examined the performance of DeepWings© under realistic conditions by processing 14,782 wing images with varying quality and produced by different operators. These images represented 2,593 colonies covering the native ranges of A. m. iberiensis (Portugal, Spain and historical introduction in the Azores), A. m. mellifera (Belgium, France, Ireland, Poland, Russia, Sweden, Switzerland, UK) and A. m. carnica (Croatia, Hungary, Romania). The classification probability obtained for the colonies was contrasted with the endemic subspecies distribution. Additionally, the association between WGM classification and that inferred from microsatellites and SNPs was evaluated for 1,214 colonies. As much as 94.4% of the wings were accepted and classified by DeepWings©. In the Iberian honey bee native range, 92,6% of the colonies were classified as A. m. iberiensis with a median probability of 91.88 (IQR = 22.52). In the Azores, 85.7% of colonies were classified as A. m. iberiensis, with a median probability of 84.16 (32.40). In the Dark honey bee native range, 41.1 % of the colonies were classified as A. m mellifera with a median probability of 99.36 (8.02). The low percentage of colonies matching the native subspecies was mainly due to the low values registered in Avignon (20.0%), Poland (32.9%), and Wales (41.2%). In contrast, most of the colonies analyzed in other locations of the native range of A. m. mellifera matched this subspecies: Belgium (100.0%), Groix (63.9%), Ouessant (72.7%), Ireland (78.0%), Russia (96.2%), Sweden (84.2%) and Switzerland (55.6%). In the colonies from Croatia, Hungary, and Romania, 88.0% of the samples were classified as A. m. carnica, with a median probability of 98.49 (6.76). The association between WGM and molecular data was highly significant but not very strong (Spearman r = 0.31, p < 0.0001). A good agreement between morphological and molecular methods was registered in samples originating from highly conserved M-lineage populations whereas in populations with historical records of foreign queen importations the agreement was weaker. In general, DeepWings© showed good performance when tested under realistic conditions. It is a valuable tool that can be used not only for honey bee breeding and conservation but also for research purposes.Estonian University of Life ScienceBiblioteca Digital do IPBAriel Yadró, CarlosRodrigues, Pedro JoãoAdam, TofilskiElen, DylanMcCormack, Grace P.Henriques, DoraPinto, M. Alice2022-10-17T10:49:32Z20222022-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10198/26007engYadró, Carlos; Rodrigues, Pedro João; Adam, Tofilski; Elen, Dylan; McCormack, Grace P.; Henriques, Dora; Pinto, M. Alice (2022). DeepWings: a machine learning tool for identification of honey bee subspecies. In Eurbee 9: 9th European Conference of Apidology. Belgradeinfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-25T12:16:50Zoai:bibliotecadigital.ipb.pt:10198/26007Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T11:44:29.356919Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv DeepWings: a machine learning tool for identification of honey bee subspecies
title DeepWings: a machine learning tool for identification of honey bee subspecies
spellingShingle DeepWings: a machine learning tool for identification of honey bee subspecies
Ariel Yadró, Carlos
Wing Geometric Morphometrics
Apis mellifera subspecies classification
Honey bee conservation
title_short DeepWings: a machine learning tool for identification of honey bee subspecies
title_full DeepWings: a machine learning tool for identification of honey bee subspecies
title_fullStr DeepWings: a machine learning tool for identification of honey bee subspecies
title_full_unstemmed DeepWings: a machine learning tool for identification of honey bee subspecies
title_sort DeepWings: a machine learning tool for identification of honey bee subspecies
author Ariel Yadró, Carlos
author_facet Ariel Yadró, Carlos
Rodrigues, Pedro João
Adam, Tofilski
Elen, Dylan
McCormack, Grace P.
Henriques, Dora
Pinto, M. Alice
author_role author
author2 Rodrigues, Pedro João
Adam, Tofilski
Elen, Dylan
McCormack, Grace P.
Henriques, Dora
Pinto, M. Alice
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Biblioteca Digital do IPB
dc.contributor.author.fl_str_mv Ariel Yadró, Carlos
Rodrigues, Pedro João
Adam, Tofilski
Elen, Dylan
McCormack, Grace P.
Henriques, Dora
Pinto, M. Alice
dc.subject.por.fl_str_mv Wing Geometric Morphometrics
Apis mellifera subspecies classification
Honey bee conservation
topic Wing Geometric Morphometrics
Apis mellifera subspecies classification
Honey bee conservation
description DeepWings© is a software that uses Machine Learning for fully automated identification of Apis mellifera subspecies based on wing geometric morphometrics (WGM). Here, we examined the performance of DeepWings© under realistic conditions by processing 14,782 wing images with varying quality and produced by different operators. These images represented 2,593 colonies covering the native ranges of A. m. iberiensis (Portugal, Spain and historical introduction in the Azores), A. m. mellifera (Belgium, France, Ireland, Poland, Russia, Sweden, Switzerland, UK) and A. m. carnica (Croatia, Hungary, Romania). The classification probability obtained for the colonies was contrasted with the endemic subspecies distribution. Additionally, the association between WGM classification and that inferred from microsatellites and SNPs was evaluated for 1,214 colonies. As much as 94.4% of the wings were accepted and classified by DeepWings©. In the Iberian honey bee native range, 92,6% of the colonies were classified as A. m. iberiensis with a median probability of 91.88 (IQR = 22.52). In the Azores, 85.7% of colonies were classified as A. m. iberiensis, with a median probability of 84.16 (32.40). In the Dark honey bee native range, 41.1 % of the colonies were classified as A. m mellifera with a median probability of 99.36 (8.02). The low percentage of colonies matching the native subspecies was mainly due to the low values registered in Avignon (20.0%), Poland (32.9%), and Wales (41.2%). In contrast, most of the colonies analyzed in other locations of the native range of A. m. mellifera matched this subspecies: Belgium (100.0%), Groix (63.9%), Ouessant (72.7%), Ireland (78.0%), Russia (96.2%), Sweden (84.2%) and Switzerland (55.6%). In the colonies from Croatia, Hungary, and Romania, 88.0% of the samples were classified as A. m. carnica, with a median probability of 98.49 (6.76). The association between WGM and molecular data was highly significant but not very strong (Spearman r = 0.31, p < 0.0001). A good agreement between morphological and molecular methods was registered in samples originating from highly conserved M-lineage populations whereas in populations with historical records of foreign queen importations the agreement was weaker. In general, DeepWings© showed good performance when tested under realistic conditions. It is a valuable tool that can be used not only for honey bee breeding and conservation but also for research purposes.
publishDate 2022
dc.date.none.fl_str_mv 2022-10-17T10:49:32Z
2022
2022-01-01T00:00:00Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10198/26007
url http://hdl.handle.net/10198/26007
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Yadró, Carlos; Rodrigues, Pedro João; Adam, Tofilski; Elen, Dylan; McCormack, Grace P.; Henriques, Dora; Pinto, M. Alice (2022). DeepWings: a machine learning tool for identification of honey bee subspecies. In Eurbee 9: 9th European Conference of Apidology. Belgrade
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Estonian University of Life Science
publisher.none.fl_str_mv Estonian University of Life Science
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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