Ruvinga, Stenford, Hunter, Gordon J.A., Duran, Olga and Nebel, Jean-Christophe (2021) Use of LSTM neural networks to identify 'queenlessness' in honeybee hives from audio signals. In: 17th International Conference on Intelligent Environments; 21 - 24 Jun 2021, Dubai, United Arab Emirates (held online).

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.

Downloads Metadata

Full text available as:

Official URL:	https://doi.org/10.1109/IE51775.2021.9486575
Item Type:	Conference or Workshop Item (Paper)
Event Title:	17th International Conference on Intelligent Environments
Organising Body:	Middlesex University Dubai
Additional Information:	Published in 2021 17th International Conference on Intelligent Environments (IE). ISBN: 9781665403467. ISSN (electronic) 2472-7571. ISSN (Print on Demand) 2469-8792.
Research Area:	Research Areas > Agriculture, veterinary and food science Research Areas > Computer science and informatics
Faculty, School or Research Centre:	Faculty of Science, Engineering and Computing Faculty of Science, Engineering and Computing > School of Computer Science and Mathematics Faculty of Science, Engineering and Computing > School of Engineering
Date Deposited:	26 Feb 2021 10:09
Last Modified:	16 Aug 2021 07:38
DOI:	https://doi.org/10.1109/IE51775.2021.9486575
URI:	https://eprints.kingston.ac.uk/id/eprint/48448

Actions (Repository Editors)

Item Control Page