Honey bees are key insect pollinators, providing important economic and ecological value
for human beings and ecosystems. This has triggered the development of several monitoring methods for assessing the temporal development of colony size, food storage, brood
and pathogens. Nonetheless, most of these methods are based on visual assessments that
are observer-dependent and prone to bias. Furthermore, the impact on colony development
(invasiveness), as well as accuracy, were rarely considered when implementing new methods.
In this study, we present and test a novel accurate and observer-independent method for
honey bee colony assessment, capable of being fully standardized. Honey bee colony size is
quantified by assessing the weight of adult bees, while brood and provision are assessed by
taking photos and conducting image analysis of the combs with the image analysis software
DeepbeeVR . The invasiveness and accuracy of the method were investigated using field data
from two experimental apiaries in Portugal, comparing results from test and control colonies.
At the end of each field experiment, most of the tested colonies had the same colony size,
brood levels and honey production as the control colonies. Nonetheless, continuous weight
data indicated some disturbance in tested colonies in the first year of monitoring. The overall accuracy of the image analysis software was improved by training, indicating that it is
possible to adapt the software to local conditions. We conclude that the use of this fully
quantitative method offers a more accurate alternative to classic visual colony assessments,
with negligible impact on colony development.
This work was supported by European Food Safety
Authority under grant OC/EFSA/SCER/2017/02; FCT provided
financial support by national funds (FCT/MCTES) to
CFE (UIDB/04004/2020) and CIMO (UIDB/00690/2020); NC
was financed by FCT under PhD grant SFRH/BD/133352/2017; YLD by DCE (Danish Centre for Environment and Energy) under grant 21628-82105.
