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The absence of the queen in a beehive is a very strong indicator of the needfor beekeeper intervention. Manually searching for the queen is an arduousrecurrent task for beekeepers that disrupts the normal life cycle of thebeehive and can be a source of stress for bees. Sound is an indicator forsignalling different states of the beehive, including the absence of the queenbee. In this work, we apply machine learning methods to automatically recognisedifferent states in a beehive using audio as input. % The system is built ontop of a method for beehive sound recognition in order to detect bee soundsfrom other external sounds. We investigate both support vector machines andconvolutional neural networks for beehive state recognition, using audio dataof beehives collected from the NU-Hive project. Results indicate the potentialof machine learning methods as well as the challenges of generalizing thesystem to new hives.

Audio-based identification of beehive states