Machine Learning Framework for the Acoustic Detection of the Queen Bee Presence

Honeybees are one of the most important pollinators in nature for both crop production and biodiversity preservation. The increase in bee mortality observed in the last decade motivated the development of continuous monitoring systems to better understand this phenomenon. Different solutions have been presented in the literature, and particularly sound analysis appears the most promising among the non-invasive techniques. In this context, we developed a machine learning framework for the analysis of the sound produced by bees for the detection of the queen bee's presence. The presence of the queen is an important indicator of the colony's health. In this work, we investigated Short Time Fourier Transform and Mel Frequency Cepstral Coefficient audio features with support vector machines and neural network classifiers. The results indicate the potential of machine learning methods for supporting the researchers' study and beekeepers in managing such important insects.