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

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dc.contributor.authorvan Dooremalen, Coby
dc.contributor.authorUlgezen, Zeynep
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, Alexis
dc.contributor.authorvan Gennip, Pim
dc.contributor.authorSchoonman, Marten
dc.contributor.authorFlener, Claude
dc.contributor.authorMatthijs, Severine
dc.contributor.authorBoúúaert, David Claeys
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, Yves
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.authorBica, João
dc.contributor.authorSimões, Sandra
dc.contributor.authorAlves da Silva, António
dc.contributor.authorCastro, Sílvia
dc.contributor.authorLoureiro, João
dc.contributor.authorHorčičková, Eva
dc.contributor.authorBencsik, Martin
dc.contributor.authorMcveigh, Adam
dc.contributor.authorKumar, Tarun
dc.contributor.authorMoro, Arrigo
dc.contributor.authorvan Delden, April
dc.contributor.authorZiółkowska, Elżbieta
dc.contributor.authorFilipiak, Michał
dc.contributor.authorMikołajczyk, Łukasz
dc.contributor.authorLeufgen, Kirsten
dc.contributor.authorde Smet, Lina
dc.contributor.authorde Graaf, Dirk C
dc.contributor.organizationWageningen University & Research, Wageningen, The Netherlandsen
dc.contributor.organizationBeeSources di Raffaele Dall’Olio, Bologna, Italyen
dc.contributor.organizationInstitut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, Avignon, Franceen
dc.contributor.organizationAarhus Universitet, Aarhus, Denmarken
dc.contributor.organizationCentre for Functional Ecology, Department of Life Sciences, TERRA Associated Laboratory, University of Coimbra, Coimbra, Portugalen
dc.contributor.organizationFriedrich-Loeffler-Institut, Bundesforschunginstitut für Tiergesundheit, Greifswald-Insel Riems, Germanyen
dc.contributor.organizationInstitute of Bee Health, University of Bern, Bern, Switzerlanden
dc.contributor.organizationStichting BEEP, Driebergen-Rijsenburg, The Netherlandsen
dc.contributor.organizationSuomen Mehiläishoitajain Liitto, Helsinki, Finlanden
dc.contributor.organizationSciensano, Brussels, Belgiumen
dc.contributor.organizationGhent University, Ghent, Belgiumen
dc.contributor.organizationMartin-Luther-Universitaet Halle-Wittenberg, Halle, Germanyen
dc.contributor.organizationUniversitatea de Stiinte Agricole si Medicina Veterinara Cluj Napoca, Cluj Napoca, Romaniaen
dc.contributor.organizationNottingham Trent University, Nottingham, UKen
dc.contributor.organizationUniwersytet Jagiellonski, Krakow, Polanden
dc.contributor.organizationSCIPROM sàrl, Saint-Sulpice, Switzerlanden
dc.date.accessioned2024-01-24T18:02:40Z
dc.date.available2024-01-24T18:02:40Z
dc.date.issued2024-01-22
dc.descriptionHoney bees are very important for nature and food production. However, beekeepers’ work is continuously challenged by pests, pathogens, pesticides, and other impacts of the environment on their honey bee colonies, and, therefore, they would greatly benefit from up-to-date insights on the health condition of their bees. To disturb those bee colonies as little as possible, it is preferable that this information be collected in an automated way. In this article, we present the B-GOOD project as a case study to monitor the health of honey bee colonies in an automated, standardized way. The use of a similar approach by researchers in their future studies would allow the combination of different datasets on bee health. More data combinations would facilitate the use of machine learning to better and more accurately determine the thresholds for beekeeper interventions, the underlying mechanisms of honey bee colony health, and the prediction of health and colony losses, among other indicators.en
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.en
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.; et al. 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 2024, 15, 76. https://doi.org/10.3390/insects15010076en
dc.identifier.doi10.3390/insects15010076
dc.identifier.urihttps://www.mdpi.com/2075-4450/15/1/76
dc.identifier.urihttps://depot.ceon.pl/handle/123456789/23726
dc.language.isoen
dc.publisherMDPIen
dc.rightsUznanie autorstwa 4.0 Międzynarodowe*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjecthoney bee automated health monitoringen
dc.subjectbeeen
dc.subjecthoney beeen
dc.subjecthealthen
dc.subjectcolonyen
dc.subjectdataen
dc.subjectcollectionen
dc.subjectstandarizationen
dc.subjectmethoden
dc.subjectprotocolen
dc.subjectstakeholderen
dc.subjectbeekeeperen
dc.subjectbeekeepingen
dc.subjectapiaryen
dc.subjectecologyen
dc.subjectwork planen
dc.subjectB-GOODen
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 Coloniesen
dc.typearticleen
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