Anomaly Detection in Beehives: An Algorithm Comparison
This work addresses anomaly detection for beekeepers to monitor hive health, but it is incremental as it compares existing methods on new data.
The paper compared multiple machine learning models for anomaly detection in beehives, focusing on events like swarming and illness, and found that a deep recurrent autoencoder performed best as a multi-purpose detector.
Sensor-equipped beehives allow monitoring the living conditions of bees. Machine learning models can use the data of such hives to learn behavioral patterns and find anomalous events. One type of event that is of particular interest to apiarists for economical reasons is bee swarming. Other events of interest are behavioral anomalies from illness and technical anomalies, e.g. sensor failure. Beekeepers can be supported by suitable machine learning models which can detect these events. In this paper we compare multiple machine learning models for anomaly detection and evaluate them for their applicability in the context of beehives. Namely we employed Deep Recurrent Autoencoder, Elliptic Envelope, Isolation Forest, Local Outlier Factor and One-Class SVM. Through evaluation with real world datasets of different hives and with different sensor setups we find that the autoencoder is the best multi-purpose anomaly detector in comparison.