A Machine Learning Approach for Queen Bee Detection Through Remote Audio Sensing to Safeguard Honeybee Colonies

Abstract

Honeybees play a pivotal role in maintaining global ecosystems and agricultural productivity through their indispensable contribution to crop pollination. However, the alarming rise in honeybee mortality, attributed to various stress factors including climate change, has highlighted the urgency of implementing effective monitoring strategies. Remote sensing of beehives emerges as a promising solution, with a focus on understanding and mitigating the impacts of these stressors. Differently from other approaches proposed in the literature, this study specifically explores the potential of lightweight machine learning models and the extraction of compressed feature to enable future deployment on microcontroller devices. The experimentation involves the application of support vector machines and neural network classifiers, considering the influence of variable audio chunk durations, the utilization of different hyperparameters and combining the audio recorded in several hives and available in different datasets.