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

TY - JOUR

T1 - 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

AU - van Dooremalen, Coby

AU - Ulgezen, Zeynep N.

AU - Dall'Olio, Raffaele

AU - Godeau, Ugoline

AU - Duan, Xiaodong

AU - Sousa, José Paulo

AU - Schäfer, Marc

AU - Beaurepaire, Alexis

AU - van Gennip, Pim

AU - Schoonman, Marten

AU - Flener, Claude

AU - Matthijs, Severine

AU - Boúúaert, David Claeys

AU - Verbeke, Wim

AU - Freshley, Dana

AU - Valkenburg, Dirk-Jan

AU - van den Bosch, Trudy

AU - Schaafsma, Famke

AU - Peters, Jeroen

AU - Xu, Mang

AU - Conte, Yves Le

AU - Alaux, Cedric

AU - Dalmon, Anne

AU - Paxton, Robert J

AU - Tehel, Anja

AU - Streicher, Tabea

AU - Dezmirean, Daniel S.

AU - Giurgiu, Alexandru L.

AU - Topping, Christopher John

AU - Williams, James Henty

AU - Capela, Nuno

AU - Lopes, Sara

AU - Alves, Fátima

AU - Alves, Joana

AU - Bica, João

AU - Simöes, Sandra

AU - Alves da Silva, António

AU - Castro, Silvia

AU - Loureiro, João

AU - Horčičková, Eva

AU - Bencsik, Martin

AU - McVeigh, Adam

AU - Kumar, Tarun

AU - Moro, Arrigo

AU - van Delden, April

AU - Ziółkowska, Elżbieta

AU - Filipiak, Michał

AU - Mikołajczyk, Łukasz

AU - Leufgen, Kirsten

AU - de Smet, Lina

AU - de Graaf, Dirk C.

PY - 2024/1

Y1 - 2024/1

N2 - 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.

AB - 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.

KW - bee data portal

KW - beekeeping

KW - big data on honey bee colonies

KW - data collection method

KW - data standardization and harmonization

KW - honey bee automated health monitoring

KW - stakeholder involvement in research

KW - work plans and protocols

UR - http://www.scopus.com/inward/record.url?scp=85183186443&partnerID=8YFLogxK

U2 - 10.3390/insects15010076

DO - 10.3390/insects15010076

M3 - Journal article

C2 - 38276825

SN - 2075-4450

VL - 15

SP - 1

EP - 22

JO - Insects

JF - Insects

IS - 1

M1 - 76

ER -