Prediction of honeybee swarms using audio signals and convolutional neural networks

Stenford Ruvinga; Gordon Hunter; Jean-Christophe Nebel; Olga Duran

doi:10.3233/AISE220032

Prediction of honeybee swarms using audio signals and convolutional neural networks

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Abstract

Honeybees are of vital importance to both agriculture and ecology. Unfortunately, their populations have been in serious decline over recent years. Swarms from hives are both of great importance to wider success of a colony and of major significance to beekeepers. In this paper, we contribute to the challenge of predicting when a swarm is going to occur. We have employed a Convolutional Neural Network (CNN) approach applied to audio data recorded from hives. Our initial results are extremely encouraging, since they allow us to distinguish hives which are preparing to swarm from those which are not with high levels of accuracy.

Original language	English
Title of host publication	Workshops at 18th International Conference on Intelligent Environments (IE2022)
Editors	Alvarez Valera, Hernan Humberto, Mitja Luštrek
Place of Publication	Amsterdam, Netherlands
Publisher	IOS Press
Pages	146-154
Number of pages	9
ISBN (Electronic)	9781643682877
ISBN (Print)	9781643682860
DOIs	https://doi.org/10.3233/AISE220032
Publication status	Published - 2022
Event	18th International Conference on Intelligent Environments - Biarritz, France Duration: 20 Jun 2022 → 23 Jun 2022

Publication series

Name	Ambient Intelligence and Smart Environments
Volume	31
ISSN (Print)	1875-4163
ISSN (Electronic)	1875-4171

Conference

Conference	18th International Conference on Intelligent Environments
Abbreviated title	IE2022
Country/Territory	France
City	Biarritz
Period	20/06/22 → 23/06/22

Keywords

Agriculture, veterinary and food science

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10.3233/AISE220032Licence: CC BY-NC

Hunter-G-51606-VoRFinal published version, 257 KBLicence: CC BY-NC

Cite this

Ruvinga, S., Hunter, G., Nebel, J.-C., & Duran, O. (2022). Prediction of honeybee swarms using audio signals and convolutional neural networks. In A. Valera, H. Humberto, & M. Luštrek (Eds.), Workshops at 18th International Conference on Intelligent Environments (IE2022) (pp. 146-154). (Ambient Intelligence and Smart Environments; Vol. 31). IOS Press. https://doi.org/10.3233/AISE220032

Ruvinga, Stenford ; Hunter, Gordon ; Nebel, Jean-Christophe et al. / Prediction of honeybee swarms using audio signals and convolutional neural networks. Workshops at 18th International Conference on Intelligent Environments (IE2022). editor / Alvarez Valera ; Hernan Humberto ; Mitja Luštrek. Amsterdam, Netherlands : IOS Press, 2022. pp. 146-154 (Ambient Intelligence and Smart Environments).

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title = "Prediction of honeybee swarms using audio signals and convolutional neural networks",

abstract = "Honeybees are of vital importance to both agriculture and ecology. Unfortunately, their populations have been in serious decline over recent years. Swarms from hives are both of great importance to wider success of a colony and of major significance to beekeepers. In this paper, we contribute to the challenge of predicting when a swarm is going to occur. We have employed a Convolutional Neural Network (CNN) approach applied to audio data recorded from hives. Our initial results are extremely encouraging, since they allow us to distinguish hives which are preparing to swarm from those which are not with high levels of accuracy.",

keywords = "Agriculture, veterinary and food science",

author = "Stenford Ruvinga and Gordon Hunter and Jean-Christophe Nebel and Olga Duran",

year = "2022",

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language = "English",

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series = "Ambient Intelligence and Smart Environments",

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pages = "146--154",

editor = "Alvarez Valera and Hernan Humberto and Mitja Lu{\v s}trek",

booktitle = "Workshops at 18th International Conference on Intelligent Environments (IE2022)",

note = "18th International Conference on Intelligent Environments, IE2022 ; Conference date: 20-06-2022 Through 23-06-2022",

}

Ruvinga, S, Hunter, G , Nebel, J-C & Duran, O 2022, Prediction of honeybee swarms using audio signals and convolutional neural networks. in A Valera, H Humberto & M Luštrek (eds), Workshops at 18th International Conference on Intelligent Environments (IE2022). Ambient Intelligence and Smart Environments, vol. 31, IOS Press, Amsterdam, Netherlands, pp. 146-154, 18th International Conference on Intelligent Environments, Biarritz, France, 20/06/22. https://doi.org/10.3233/AISE220032

Prediction of honeybee swarms using audio signals and convolutional neural networks. / Ruvinga, Stenford; Hunter, Gordon ; Nebel, Jean-Christophe et al.
Workshops at 18th International Conference on Intelligent Environments (IE2022). ed. / Alvarez Valera; Hernan Humberto; Mitja Luštrek. Amsterdam, Netherlands: IOS Press, 2022. p. 146-154 (Ambient Intelligence and Smart Environments; Vol. 31).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Prediction of honeybee swarms using audio signals and convolutional neural networks

AU - Ruvinga, Stenford

AU - Hunter, Gordon

AU - Nebel, Jean-Christophe

AU - Duran, Olga

PY - 2022

Y1 - 2022

N2 - Honeybees are of vital importance to both agriculture and ecology. Unfortunately, their populations have been in serious decline over recent years. Swarms from hives are both of great importance to wider success of a colony and of major significance to beekeepers. In this paper, we contribute to the challenge of predicting when a swarm is going to occur. We have employed a Convolutional Neural Network (CNN) approach applied to audio data recorded from hives. Our initial results are extremely encouraging, since they allow us to distinguish hives which are preparing to swarm from those which are not with high levels of accuracy.

AB - Honeybees are of vital importance to both agriculture and ecology. Unfortunately, their populations have been in serious decline over recent years. Swarms from hives are both of great importance to wider success of a colony and of major significance to beekeepers. In this paper, we contribute to the challenge of predicting when a swarm is going to occur. We have employed a Convolutional Neural Network (CNN) approach applied to audio data recorded from hives. Our initial results are extremely encouraging, since they allow us to distinguish hives which are preparing to swarm from those which are not with high levels of accuracy.

KW - Agriculture, veterinary and food science

U2 - 10.3233/AISE220032

DO - 10.3233/AISE220032

M3 - Conference contribution

SN - 9781643682860

T3 - Ambient Intelligence and Smart Environments

SP - 146

EP - 154

BT - Workshops at 18th International Conference on Intelligent Environments (IE2022)

A2 - Valera, Alvarez

A2 - Humberto, Hernan

A2 - Luštrek, Mitja

PB - IOS Press

CY - Amsterdam, Netherlands

T2 - 18th International Conference on Intelligent Environments

Y2 - 20 June 2022 through 23 June 2022

ER -

Ruvinga S, Hunter G , Nebel JC , Duran O. Prediction of honeybee swarms using audio signals and convolutional neural networks. In Valera A, Humberto H, Luštrek M, editors, Workshops at 18th International Conference on Intelligent Environments (IE2022). Amsterdam, Netherlands: IOS Press. 2022. p. 146-154. (Ambient Intelligence and Smart Environments). doi: 10.3233/AISE220032

Prediction of honeybee swarms using audio signals and convolutional neural networks

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