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: Contribution to conference › Paper › peer-review

10 Downloads (Pure)

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
DOIs	https://doi.org/10.3233/AISE220032
Publication status	Published - Jun 2022
Event	Workshop on Edge AI for Smart Agriculture (EAISA 2022) - Biarritz, France Duration: 20 Jun 2022 → 23 Jun 2022

Workshop

Workshop	Workshop on Edge AI for Smart Agriculture (EAISA 2022)
Period	20/06/22 → 23/06/22

Bibliographical note

Note: Published in: Alvarez Valera, Humberto Hernan and Luštrek, Mitja (eds.) (2022) Workshops at 18th International Conference on Intelligent Environments (IE2022). Amsterdam, Netherlands : IOS Press BV. pp.146-154. (Ambient Intelligence and Smart Environments, volume 31) ISSN (print) 1875-4163 ISSN (online) 1875-4171 ISBN (print) 9781643682860 ISBN (online) 9781643682877.

Keywords

Agriculture, veterinary and food science

Access to Document

10.3233/AISE220032

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

https://doi.org/10.3233/AISE220032

Cite this

@conference{cf44def1e6fc482aa7c97f4679f75ed8,

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",

note = "Note: Published in: Alvarez Valera, Humberto Hernan and Lu{\v s}trek, Mitja (eds.) (2022) Workshops at 18th International Conference on Intelligent Environments (IE2022). Amsterdam, Netherlands : IOS Press BV. pp.146-154. (Ambient Intelligence and Smart Environments, volume 31) ISSN (print) 1875-4163 ISSN (online) 1875-4171 ISBN (print) 9781643682860 ISBN (online) 9781643682877.; Workshop on Edge AI for Smart Agriculture (EAISA 2022) ; Conference date: 20-06-2022 Through 23-06-2022",

year = "2022",

month = jun,

doi = "10.3233/AISE220032",

language = "English",

}

TY - CONF

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

N1 - Note: Published in: Alvarez Valera, Humberto Hernan and Luštrek, Mitja (eds.) (2022) Workshops at 18th International Conference on Intelligent Environments (IE2022). Amsterdam, Netherlands : IOS Press BV. pp.146-154. (Ambient Intelligence and Smart Environments, volume 31) ISSN (print) 1875-4163 ISSN (online) 1875-4171 ISBN (print) 9781643682860 ISBN (online) 9781643682877.

PY - 2022/6

Y1 - 2022/6

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

UR - https://sites.google.com/view/eaisa2022/home

U2 - 10.3233/AISE220032

DO - 10.3233/AISE220032

M3 - Paper

T2 - Workshop on Edge AI for Smart Agriculture (EAISA 2022)

Y2 - 20 June 2022 through 23 June 2022

ER -

Prediction of honeybee swarms using audio signals and convolutional neural networks

Abstract

Workshop

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Prediction of honeybee swarms using audio signals and convolutional neural networks

Cite this