Non-Invasive Spectral-Based Swarm Detection

A large number of honey beehives have been lost in recent years due to causes that are often collectively referred to as Colony Collapse Disorder. Due to the importance of honey bees in the food chain as one of the most efficient pollinators, there has been a notable growth in research involving precision apiculture. Our project, Appalachian Multi-purpose Apiary Informatics System (AppMAIS), was funded to establish 24 hives at 12 locations in Western North Carolina in order to collect data such as: humidity, temperature, weight, video, and audio recordings for monitoring purposes. By taking into account several key bioacoustic hive health indicators previously identified by the Apiary research community, we sought to identify the collapse of beehives primarily via the auditory domain. This paper provides some of the preliminary results for the audio analysis portion of the project (specifically unsupervised anomaly detection) via the use of Non-Negative Matrix Factorization (NMF) based approaches and an ensemble Minimum Covariance Determinant (MCD) estimator.