Audio-Based Identification of Queen Bee Presence Inside Beehives

Honeybees are essential for the health of people and the planet. They play a key role in the pollination of most crops. The high mortality observed in the last decade, caused by stress factors among which the climate change, have raised the necessity of remote sensing the beehives to help monitor the health of honeybees and better understand this phenomenon. Several solutions have been proposed in the literature, and some of them include the analysis of in-hive sounds. In this scenario, we explore the potential of machine learning methods for queen bee detection using only the audio signal, being a good indicator of the colony state of health. In particular, we experiment support vector machines and neural network classifiers. We consider the effect of varying the audio chunk duration and the adoption of different hyperparameters.