Abstract
We present a novel system for the automatic video monitoring of honey bee foraging activity at the hive entrance. This monitoring system is built upon convolutional neural networks that perform multiple animal pose estimation without the need for marking. This precise detection of honey bee body parts is a key element of the system to provide detection of entrance and exit events at the entrance of the hive including accurate pollen detection. A detailed evaluation of the quality of the detection and a study of the effect of the parameters are presented. The complete system also integrates identification of barcode marked bees, which enables the monitoring at both aggregate and individual levels. The results obtained on multiple days of video recordings show the applicability of the approach for large-scale deployment. This is an important step forward for the understanding of complex behaviors exhibited by honey bees and the automatic assessment of colony health.
Highlights
There is a growing interest in the quantification of behavior in honey bees (Apis Melifera)
As an equivalent for honey bee pose estimation, we considered that a key-point is correctly detected if its residual is less than half of the distance between thorax and head
We presented a new system for the automatic surveillance of honey bees at the hive entrance using machine learning and computer vision and applied them to implement an end-to-end pipeline that quantifies their foraging behavior
Summary
There is a growing interest in the quantification of behavior in honey bees (Apis Melifera). Honey bee colonies exhibit complex selfregulatory behaviors that are not yet fully understood This includes how colonies maintain homeostasis or adapt to environmental changes, automatic adjustment of circadian patterns based on thermal cycles (Giannoni-Guzmán et al, 2021), thermo-regulation (Kaspar et al, 2018), or the individual variation in foraging activities in function of the season of the year (Meikle and Holst, 2016). Such studies may benefit greatly from automatic surveillance systems of the hives to detect both individual and collective behavior continuously over days or even seasons to provide crucial insights about biological mechanisms that express themselves over such time frames
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