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Anomaly Detection in Beehives: An Algorithm Comparison

, , , , and . Sensor Networks, page 1--20. Cham, Springer International Publishing, (2022)

Abstract

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.

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Anomaly Detection in Beehives: An Algorithm Comparison | SpringerLink

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