  Honey bees are critical to our ecosystem and food security as a pollinator,
contributing 35% of our global agriculture yield. In spite of their importance,
beekeeping is exclusively dependent on human labor and experience-derived
heuristics, while requiring frequent human checkups to ensure the colony is
healthy, which can disrupt the colony. Increasingly, pollinator populations are
declining due to threats from climate change, pests, environmental toxicity,
making their management even more critical than ever before in order to ensure
sustained global food security. To start addressing this pressing challenge, we
developed an integrated hardware sensing system for beehive monitoring through
audio and environment measurements, and a hierarchical semi-supervised deep
learning model, composed of an audio modeling module and a predictor, to model
the strength of beehives. The model is trained jointly on audio reconstruction
and prediction losses based on human inspections, in order to model both
low-level audio features and circadian temporal dynamics. We show that this
model performs well despite limited labels, and can learn an audio embedding
that is useful for characterizing different sound profiles of beehives. This is
the first instance to our knowledge of applying audio-based deep learning to
model beehives and population size in an observational setting across a large
number of hives.
