دورية أكاديمية
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
| العنوان: | 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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| المؤلفون: | van Dooremalen, Coby, Ulgezen, Zeynep, Dall’olio, Raffaele, Godeau, Ugoline, Duan, Xiaodong, Sousa, José Paulo, Schäfer, Marc O, Beaurepaire, Alexis, van Gennip, Pim, Schoonman, Marten, Flener, Claude, Matthijs, Severine, Boúúaert, David Claeys, Verbeke, Wim, Freshley, Dana, Valkenburg, Dirk-Jan, van den Bosch, Trudy, Schaafsma, Famke, Peters, Jeroen, Xu, Mang, Le Conte, Yves, Alaux, Cedric, Dalmon, Anne, Paxton, Robert J, Tehel, Anja, Streicher, Tabea, Dezmirean, Daniel S, Giurgiu, Alexandru I, Topping, Christopher J, Williams, James Henty, Capela, Nuno, Lopes, Sara, Alves, Fátima, Alves, Joana, Bica, João, Simões, Sandra, Alves da Silva, António, Castro, Sílvia, Loureiro, João, Horčičková, Eva, Bencsik, Martin, Mcveigh, Adam, Kumar, Tarun, Moro, Arrigo, van Delden, April, Ziółkowska, Elżbieta, Filipiak, Michał, Mikołajczyk, Łukasz, Leufgen, Kirsten, de Smet, Lina, de Graaf, Dirk C |
| المساهمون: | Wageningen University & Research, Wageningen, The Netherlands, BeeSources di Raffaele Dall’Olio, Bologna, Italy, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, Avignon, France, Aarhus Universitet, Aarhus, Denmark, Centre for Functional Ecology, Department of Life Sciences, TERRA Associated Laboratory, University of Coimbra, Coimbra, Portugal, Friedrich-Loeffler-Institut, Bundesforschunginstitut für Tiergesundheit, Greifswald-Insel Riems, Germany, Institute of Bee Health, University of Bern, Bern, Switzerland, Stichting BEEP, Driebergen-Rijsenburg, The Netherlands, Suomen Mehiläishoitajain Liitto, Helsinki, Finland, Sciensano, Brussels, Belgium, Ghent University, Ghent, Belgium, Martin-Luther-Universitaet Halle-Wittenberg, Halle, Germany, Universitatea de Stiinte Agricole si Medicina Veterinara Cluj Napoca, Cluj Napoca, Romania, Nottingham Trent University, Nottingham, UK, Uniwersytet Jagiellonski, Krakow, Poland, SCIPROM sàrl, Saint-Sulpice, Switzerland |
| بيانات النشر: | MDPI |
| سنة النشر: | 2024 |
| المجموعة: | Centre for Open Science: CeON Repository |
| مصطلحات موضوعية: | honey bee automated health monitoring, bee, honey bee, health, colony, data, collection, standarization, method, protocol, stakeholder, beekeeper, beekeeping, apiary, ecology, work plan, B-GOOD |
| الوصف: | Honey 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. ; Honey 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 ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | van 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/insects15010076Test; https://www.mdpi.com/2075-4450/15/1/76Test; https://depot.ceon.pl/handle/123456789/23726Test |
| DOI: | 10.3390/insects15010076 |
| الإتاحة: | https://doi.org/10.3390/insects15010076Test https://depot.ceon.pl/handle/123456789/23726Test https://www.mdpi.com/2075-4450/15/1/76Test |
| حقوق: | info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: | edsbas.E4B70CE8 |
| قاعدة البيانات: | BASE |
| DOI: | 10.3390/insects15010076 |
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