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Journal article
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 (Basel, Switzerland), Vol.15(1), 76
2024
PMID: 38276825
Abstract
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 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.
Details
- Title
- 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
- Authors/Creators
- Coby van Dooremalen - Wageningen University & ResearchZeynep N. Ulgezen - Wageningen University & ResearchRaffaele Dall'Olio - BeeSources Raffaele DallOlio, I-40132 Bologna, ItalyUgoline Godeau - Institut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementXiaodong Duan - Aarhus UniversityJose Paulo Sousa - University of CoimbraMarc O. Schaefer - Friedrich Loeffler Inst, Bundesforschunginst Tiergesundheit, D-17493 Greifswald Insel Riems, GermanyAlexis Beaurepaire - University of BernPim van Gennip - Korps Landelijke PolitiedienstenMarten Schoonman - Korps Landelijke PolitiedienstenClaude Flener - Finnish Dental AssociationSeverine Matthijs - SciensanoDavid Claeys Bouuaert - Ghent University HospitalWim Verbeke - Ghent University HospitalDana Freshley - Ghent University HospitalDirk-Jan Valkenburg - Wageningen University & ResearchTrudy van den Bosch - Wageningen University & ResearchFamke Schaafsma - Wageningen University & ResearchJeroen Peters - Wageningen University & ResearchMang Xu - Wageningen University & ResearchYves Le Conte - Institut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementCedric Alaux - Inst Natl Rech Agr Alimentat & Environm, F-84914 Avignon, FranceAnne Dalmon - Institut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementRobert J. Paxton - Martin Luther University Halle-WittenbergAnja Tehel - Martin Luther Univ Halle Wittenberg, D-06120 Halle, GermanyTabea Streicher - Martin Luther University Halle-WittenbergDaniel S. Dezmirean - University of Agricultural Sciences and Veterinary Medicine of Cluj-NapocaAlexandru I. Giurgiu - University of Agricultural Sciences and Veterinary Medicine of Cluj-NapocaChristopher J. Topping - Aarhus UniversityJames Henty Williams - Aarhus University (Denmark)Nuno Capela - University of CoimbraSara Lopes - University of CoimbraFatima Alves - University of CoimbraJoana Alves - University of CoimbraJoao Bica - University of CoimbraSandra Simoes - University of CoimbraAntonio Alves da Silva - University of CoimbraSilvia Castro - University of CoimbraJoao Loureiro - University of CoimbraEva Horcickova - University of CoimbraMartin Bencsik - Nottingham Trent UniversityAdam Mcveigh - Murdoch University, Harry Butler InstituteTarun Kumar - Nottingham Trent UniversityArrigo Moro - University of BernApril van Delden - Korps Landelijke PolitiedienstenElzbieta Ziolkowska - Uniwersytet Jagiellonski, PL-30387 Krakow, PolandMichal Filipiak - Uniwersytet Jagiellonski, PL-30387 Krakow, PolandLukasz Mikolajczyk - Uniwersytet Jagiellonski, PL-30387 Krakow, PolandKirsten Leufgen - ScipromLina De Smet - Ghent University HospitalDirk C. de Graaf - Ghent University
- Publication Details
- Insects (Basel, Switzerland), Vol.15(1), 76
- Publisher
- MDPI
- Number of pages
- 22
- Grant note
- European Union's Horizon 2020 Research AMP; Innovation Program
- Identifiers
- 991005802450307891
- Copyright
- © 2024 by the authors.
- Murdoch Affiliation
- Harry Butler Institute; Murdoch University
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 3 Agriculture, Environment & Ecology
- 3.32 Entomology
- 3.32.750 Bee Ecology
- Web Of Science research areas
- Entomology
- ESI research areas
- Plant & Animal Science