The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring

Abstract

The vital role of honeybees in pollination and their high rate of mortality in the last decade have raised concern among beekeepers and researchers alike. As such, robust and remote sensing of beehives has emerged as a potential tool to help monitor the health of honeybees. Over the last decade, several monitoring systems have been proposed, including those based on in-hive acoustics. Despite its popularity, existing audio-based systems do not take context into account (e.g., environmental noise factors), and thus the performance may be severely hampered when deployed. In this paper, we investigate the effect that three different environmental noise factors (i.e., nearby train rail squealing, beekeeper speech, and rain noise) can have on three acoustic features (i.e., spectrogram, mel frequency cepstral coefficients, and discrete wavelet coefficients) used in existing automated beehive monitoring systems. To this end, audio data were collected continuously over a period of three months (August, September, and October) in 2021 from 11 urban beehives located in downtown Montréal, Québec, Canada. A system based on these features and a convolutional neural network was developed to predict beehive strength, an indicator of the size of the colony. Results show the negative impact that environmental factors can have across all tested features, resulting in an increase of up to 355% in mean absolute prediction error when heavy rain was present.