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Bridging the gap between field experiments and machine learning: the EC H2020 B-GOOD Project as a case study towards automated predictive health monitoring of honey bee colonies

2024-01-22Journal article Open access
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datacite.subject.fosCiências Sociais
datacite.subject.fosCiências Sociais::Sociologia
datacite.subject.fosCiências Sociais::Outras Ciências Sociais
datacite.subject.sdg15:Proteger a Vida Terrestre
dc.contributor.authorVan Dooremalen, Coby
dc.contributor.authorUlgezen, Zeynep N.
dc.contributor.authorDall’Olio, Raffaele
dc.contributor.authorGodeau, Ugoline
dc.contributor.authorDuan, Xiaodong
dc.contributor.authorSousa, José Paulo
dc.contributor.authorSchäfer, Marc O.
dc.contributor.authorBeaurepaire, Alexi
dc.contributor.authorVan Gennip, Pim
dc.contributor.authorSchoonman, Marten
dc.contributor.authorFlener, Claude
dc.contributor.authorMatthijs, Severine
dc.contributor.authorClaeys Boúúaert, David
dc.contributor.authorVerbeke, Wim
dc.contributor.authorFreshley, Dana
dc.contributor.authorValkenburg, Dirk-Jan
dc.contributor.authorVan Den Bosch, Trudy
dc.contributor.authorSchaafsma, Famke
dc.contributor.authorPeters, Jeroen
dc.contributor.authorXu, Mang
dc.contributor.authorLe Conte, Yyes
dc.contributor.authorAlaux, Cedric
dc.contributor.authorDalmon, Anne
dc.contributor.authorPaxton, Robert J.
dc.contributor.authorTehel, Anja
dc.contributor.authorStreicher, Tabea
dc.contributor.authorDezmirean, Daniel S.
dc.contributor.authorGiurgiu, Alexandru I.
dc.contributor.authorTopping, Christopher J.
dc.contributor.authorWilliams, James Henty
dc.contributor.authorCapela, Nuno
dc.contributor.authorLopes, Sara
dc.contributor.authorAlves, Fátima
dc.contributor.authorAlves, Joana
dc.contributor.authoret al.
dc.contributor.authorAlves, Fátima
dc.date.accessioned2026-01-06T10:58:14Z
dc.date.available2026-01-06T10:58:14Z
dc.date.issued2024-01-22
dc.description.abstractHoney bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies’ exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony’s health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project’s data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.eng
dc.identifier.citationVan Dooremalen, C., Ulgezen, Z. N., Dall’Olio, R., Godeau, U., Duan, X., Sousa, J. P., Schäfer, M. O., Beaurepaire, A., Van Gennip, P., Schoonman, M., Flener, C., Matthijs, S., Claeys Boúúaert, D., Verbeke, W., Freshley, D., Valkenburg, D.-J., Van Den Bosch, T., Schaafsma, F., Peters, J., … De Graaf, D. C. (2024). Bridging the gap between field experiments and machine learning: The EC H2020 B-GOOD Project as a case study towards automated predictive health monitoring of honey bee colonies. Insects, 15(1), 1–22. https://doi.org/10.3390/insects15010076
dc.identifier.doi10.3390/insects15010076
dc.identifier.issn2075-4450
dc.identifier.urihttp://hdl.handle.net/10400.2/20645
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relation.hasversionhttps://www.mdpi.com/2075-4450/15/1/76
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleBridging the gap between field experiments and machine learning: the EC H2020 B-GOOD Project as a case study towards automated predictive health monitoring of honey bee colonieseng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage22
oaire.citation.issue1
oaire.citation.startPage1
oaire.citation.titleInsects
oaire.citation.volume15
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameAlves
person.givenNameFátima
person.identifier.ciencia-idF41D-6E75-A58D
person.identifier.orcid0000-0003-2600-8652
relation.isAuthorOfPublication01db740c-0644-4274-a03f-4c348c8b8ac5
relation.isAuthorOfPublication.latestForDiscovery01db740c-0644-4274-a03f-4c348c8b8ac5

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Extensão do Centro de Ecologia Funcional da Universidade de Coimbra na Universidade Aberta | Artigos em revistas internacionais / Papers in international journals