Remote detection of the swarming of honey bee colonies by single-point temperature monitoring

Zacepins, Aleksejs; Kviesis, Armands; Stalidzāns, Egils; Liepniece, M.; Meitalovs, Jurijs

doi:10.1016/j.biosystemseng.2016.05.012

Cited by 43 publications

(33 citation statements)

References 18 publications

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“…Through visual analysis of the data, we labeled each day of the dataset as either normal or anomalous. According to (Zacepins et al, 2016;Ferrari et al, 2008) a fully enlarged colony maintains a constant core temperature of 34.5 • C. All days with much higher or lower temperature readings were considered to contain anomalies. The training and validation set is sampled from the normal portion of the dataset.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Anomaly Detection in Beehives using Deep Recurrent Autoencoders

Davidson

Steininger

Lautenschlager

et al. 2020

Proceedings of the 9th International Conference on Sensor Networks

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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.

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Section: Methodsmentioning

confidence: 99%

“…Predictions. The rule-based algorithm described in (Zacepins et al, 2016) (referred to as RBA) was used on all subsets. That is, it was used on the training and testing data from the colonies in Bad Schwartau, Würzburg and Markt Indersdorf, as well as the one in Jelgava itself.…”

Section: Methodsmentioning

confidence: 99%

Anomaly Detection in Beehives using Deep Recurrent Autoencoders

Davidson

Steininger

Lautenschlager

et al. 2020

Proceedings of the 9th International Conference on Sensor Networks

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show abstract

“…Zacepins (Zacepins et al, 2016) focused on how to help beekeepers remotely monitor bee colonies to identify the status of bee swarming. To identify this condition, they monitored 10 bee families in their hives for 4 months.…”

Section: Methodsmentioning

confidence: 99%

Design and Realization of Interconnection of Multifunctional Weighing Device with Sigfox Data Network

Magdin¹,

Valovič²,

Koprda³

et al. 2020

AOL

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With the increasing expansion of IoT networks, people are trying to connect more and more devices to data networks. Nowadays, such devices are made possible by platforms such as Arduino and Espressif. In recent years, one of the areas of interest for IoT has been the development and maintenance of beekeeping. Although there are various commercial solutions for multifunctional hive scales (BeeSpy or Alya), they are quite expensive. In this paper, we present a hardware and software solution to this problem. From a hardware point of view, our goal was to design such a device that would be competitive, capture various data using ambient sensors, and communicate over the new SigFox data network that was introduced in Slovakia in 2018. From a software point of view, we aimed to design and implement an application for smartphones. With this application, it is possible to fully visualize all acquired sensor data. The communication between the multifunction weighing device and the user's smartphone is ensured via SMS messages.

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“…Further studies relying on bee sounds revealed they could be indicative of a swarm being imminent [5], [6]. Alternatively, other authors [7], [8], [9] noted that a rising temperature inside a hive could predict swarms. With regard to the problem of queen lessness in a hive, [10] used a method based on a Fast Fourier Transform (FFT) and Self-Organising Map (SOM) to investigate how the sounds produced by honeybees in hives changed after their queen had been removed, comparing those acoustic data with those produced in nearby "control" hives where the queen was allowed to remain, under identical local environmental conditions.…”

Section: Related Previous Workmentioning

confidence: 99%

Use of LSTM Networks to Identify “Queenlessness” in Honeybee Hives from Audio Signals

Ruvinga

Hunter

Durán

et al. 2021

2021 17th International Conference on Intelligent Environments (IE)

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Honeybees are of vital importance to both agriculture and ecology, but honeybee populations have been in serious decline over recent years. The queen bee is of crucial importance to the success of a colony. In this paper, we contribute to addressing these problems by employing Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP) Neural Networks and Logistic Regression approaches applied to audio data recorded from "queen-absent" and "queen-present" hives to provide a method of prompt detection of a hive lacking a queen. The initial resultsparticularly from the LSTM -are highly encouraging.

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Remote detection of the swarming of honey bee colonies by single-point temperature monitoring

Cited by 43 publications

References 18 publications

Anomaly Detection in Beehives using Deep Recurrent Autoencoders

Anomaly Detection in Beehives using Deep Recurrent Autoencoders

Design and Realization of Interconnection of Multifunctional Weighing Device with Sigfox Data Network

Use of LSTM Networks to Identify “Queenlessness” in Honeybee Hives from Audio Signals

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