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Authors: Padraig Davidson ; Michael Steininger ; Florian Lautenschlager ; Konstantin Kobs ; Anna Krause and Andreas Hotho

Affiliation: Institute of Computer Science, Chair of Computer Science X, University of Würzburg, Am Hubland, Würzburg, Germany

Keyword(s): Precision Beekeeping, Anomaly Detection, Deep Learning, Autoencoder, Swarming.

Abstract: Precision beekeeping allows to monitor bees’ living conditions by equipping beehives with sensors. The data recorded by these hives can be analyzed by machine learning models to learn behavioral patterns of or search for unusual events in bee colonies. One typical target is the early detection of bee swarming as apiarists want to avoid this due to economical reasons. Advanced methods should be able to detect any other unusual or abnormal behavior arising from illness of bees or from technical reasons, e.g. sensor failure. In this position paper we present an autoencoder, a deep learning model, which detects any type of anomaly in data independent of its origin. Our model is able to reveal the same swarms as a simple rule-based swarm detection algorithm but is also triggered by any other anomaly. We evaluated our model on real world data sets that were collected on different hives and with different sensor setups.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Davidson, P.; Steininger, M.; Lautenschlager, F.; Kobs, K.; Krause, A. and Hotho, A. (2020). Anomaly Detection in Beehives using Deep Recurrent Autoencoders. In Proceedings of the 9th International Conference on Sensor Networks - SENSORNETS; ISBN 978-989-758-403-9; ISSN 2184-4380, SciTePress, pages 142-149. DOI: 10.5220/0009161201420149

@conference{sensornets20,
author={Padraig Davidson. and Michael Steininger. and Florian Lautenschlager. and Konstantin Kobs. and Anna Krause. and Andreas Hotho.},
title={Anomaly Detection in Beehives using Deep Recurrent Autoencoders},
booktitle={Proceedings of the 9th International Conference on Sensor Networks - SENSORNETS},
year={2020},
pages={142-149},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009161201420149},
isbn={978-989-758-403-9},
issn={2184-4380},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Sensor Networks - SENSORNETS
TI - Anomaly Detection in Beehives using Deep Recurrent Autoencoders
SN - 978-989-758-403-9
IS - 2184-4380
AU - Davidson, P.
AU - Steininger, M.
AU - Lautenschlager, F.
AU - Kobs, K.
AU - Krause, A.
AU - Hotho, A.
PY - 2020
SP - 142
EP - 149
DO - 10.5220/0009161201420149
PB - SciTePress

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