# Anomaly Detection in Beehives using Deep Recurrent Autoencoders

arXiv: https://arxiv.org/abs/2003.04576 · HTML: https://mlsanity.com/paper/2003.04576/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [Anomaly Detection in Beehives: An Algorithm Comparison](https://mlsanity.com/paper/2110.03945/) (2021)
- [Semi-unsupervised Learning for Time Series Classification](https://mlsanity.com/paper/2207.03119/) (2022)
- [EBV: Electronic Bee-Veterinarian for Principled Mining and Forecasting of Honeybee Time Series](https://mlsanity.com/paper/2402.01902/) (2024)

## Abstract (arXiv)

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.