Use of LSTM neural networks to identify 'queenlessness' in honeybee hives from audio signals

Stenford Ruvinga; Gordon J.A. Hunter; Olga Duran; Jean Christophe Nebel

doi:10.1109/IE51775.2021.9486575

Use of LSTM neural networks to identify 'queenlessness' in honeybee hives from audio signals

Kingston University

Research output: Contribution to conference › Paper › peer-review

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Abstract

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 make a contribution to addressing these problems by employing LSTM, Multi-Layer Perceptron Neural Networks and Logistic Regression approaches applied to audio data recorded from ‟queen-less” and ‟queen-right” hives to provide a method of prompt detection of a hive lacking a healthy queen. The initial results - particularly from the LSTM - are highly encouraging.

Original language	English
DOIs	https://doi.org/10.1109/IE51775.2021.9486575
Publication status	Published - 2021
Event	17th International Conference on Intelligent Environments - Dubai, United Arab Emirates (held online) Duration: 21 Jun 2021 → 24 Jun 2021

Conference

Conference	17th International Conference on Intelligent Environments
Period	21/06/21 → 24/06/21

Bibliographical note

Note: Published in 2021 17th International Conference on Intelligent Environments (IE). ISBN: 9781665403467. ISSN (electronic) 2472-7571. ISSN (Print on Demand) 2469-8792.

Organising Body: Middlesex University Dubai

Keywords

Agriculture, veterinary and food science

Access to Document

10.1109/IE51775.2021.9486575

Ruvinga-S-48448-AAMAccepted author manuscript, 499 KBLicence: Unspecified

https://doi.org/10.1109/IE51775.2021.9486575

Use of LSTM neural networks to identify 'queenlessness' in honeybee hives from audio signals
Ruvinga, S., Hunter, G. J. A., Duran, O. & Nebel, J.-C., 2021, Published in 2021 17th International Conference on Intelligent Environments (IE). ISBN: 9781665403467. ISSN (electronic) 2472-7571. ISSN (Print on Demand) 2469-8792. Organising Body: Middlesex University Dubai Organising Body: Middlesex University Dubai.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Cite this

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title = "Use of LSTM neural networks to identify 'queenlessness' in honeybee hives from audio signals",

abstract = "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 make a contribution to addressing these problems by employing LSTM, Multi-Layer Perceptron Neural Networks and Logistic Regression approaches applied to audio data recorded from ‟queen-less” and ‟queen-right” hives to provide a method of prompt detection of a hive lacking a healthy queen. The initial results - particularly from the LSTM - are highly encouraging.",

keywords = "Agriculture, veterinary and food science",

author = "Stenford Ruvinga and Hunter, \{Gordon J.A.\} and Olga Duran and Nebel, \{Jean Christophe\}",

note = "Note: Published in 2021 17th International Conference on Intelligent Environments (IE). ISBN: 9781665403467. ISSN (electronic) 2472-7571. ISSN (Print on Demand) 2469-8792. Organising Body: Middlesex University Dubai; 17th International Conference on Intelligent Environments ; Conference date: 21-06-2021 Through 24-06-2021",

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doi = "10.1109/IE51775.2021.9486575",

language = "English",

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AU - Ruvinga, Stenford

AU - Hunter, Gordon J.A.

AU - Duran, Olga

AU - Nebel, Jean Christophe

N1 - Note: Published in 2021 17th International Conference on Intelligent Environments (IE). ISBN: 9781665403467. ISSN (electronic) 2472-7571. ISSN (Print on Demand) 2469-8792. Organising Body: Middlesex University Dubai

PY - 2021

Y1 - 2021

N2 - 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 make a contribution to addressing these problems by employing LSTM, Multi-Layer Perceptron Neural Networks and Logistic Regression approaches applied to audio data recorded from ‟queen-less” and ‟queen-right” hives to provide a method of prompt detection of a hive lacking a healthy queen. The initial results - particularly from the LSTM - are highly encouraging.

AB - 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 make a contribution to addressing these problems by employing LSTM, Multi-Layer Perceptron Neural Networks and Logistic Regression approaches applied to audio data recorded from ‟queen-less” and ‟queen-right” hives to provide a method of prompt detection of a hive lacking a healthy queen. The initial results - particularly from the LSTM - are highly encouraging.

KW - Agriculture, veterinary and food science

U2 - 10.1109/IE51775.2021.9486575

DO - 10.1109/IE51775.2021.9486575

M3 - Paper

T2 - 17th International Conference on Intelligent Environments

Y2 - 21 June 2021 through 24 June 2021

ER -

Use of LSTM neural networks to identify 'queenlessness' in honeybee hives from audio signals

Abstract

Conference

Bibliographical note

Keywords

Access to Document

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Research output

Use of LSTM neural networks to identify 'queenlessness' in honeybee hives from audio signals

Cite this