  The absence of the queen in a beehive is a very strong indicator of the need
for beekeeper intervention. Manually searching for the queen is an arduous
recurrent task for beekeepers that disrupts the normal life cycle of the
beehive and can be a source of stress for bees. Sound is an indicator for
signalling different states of the beehive, including the absence of the queen
bee. In this work, we apply machine learning methods to automatically recognise
different states in a beehive using audio as input. % The system is built on
top of a method for beehive sound recognition in order to detect bee sounds
from other external sounds. We investigate both support vector machines and
convolutional neural networks for beehive state recognition, using audio data
of beehives collected from the NU-Hive project. Results indicate the potential
of machine learning methods as well as the challenges of generalizing the
system to new hives.
