Remote and automated detection of Asian hornets (Vespa velutina nigrithorax) at an apiary, using spectral features of their hovering flight sounds

Hall, H, Bencsik, M ORCID: https://orcid.org/0000-0002-6278-0378, Capela, N, Sousa, JP and de Graaf, DC, 2025. Remote and automated detection of Asian hornets (Vespa velutina nigrithorax) at an apiary, using spectral features of their hovering flight sounds. Computers and Electronics in Agriculture, 235: 110307. ISSN 0168-1699

Preview

Text 2419688_Bencsik.pdf - Published version Download (4MB) | Preview

Abstract

Asian hornets (Vespa velutina nigrithorax) are an invasive species that have spread across Europe since 2004. As V.velutina largely predate on honeybees, assessing their presence at apiaries would be useful for invasive species control programmes and beekeepers to help protect their hives. At present, hornet monitoring techniques are both costly and time consuming. A promising alternative is a remote detection strategy at apiaries, which would promote straightforward, non-invasive data acquisition. The remote capture of flight acoustics should benefit hornet detection as wingbeat frequencies have previously been described as ‘the fingerprint’ of some flying invertebrate species. We here demonstrate a non-invasive method of V.velutina detection using their hovering flight sounds, captured by microphones that can be left at an apiary over the long-term. Paired with a training algorithm (principal component analysis and discriminant function analysis) that discriminates between hornet flight and other external noises (honeybee flight sounds and general background noise), we demonstrate that hornet hovering acoustics exhibit specific spectral features that promote the detection of individuals at an apiary. The training algorithm in our study was highly accurate (98.7 %) when testing just under 1-hour of apiary recordings. Utilising two-dimensional-Fourier-transforms has also benefited this algorithm, as the analysis technique is ideal for identifying repeating features in sound/vibrational data, which are an inherent consequence of hovering hornet sounds. The experimental design and training algorithm used in this study have demonstrated excellent potential for hornet detection in the field, and are now ready to be tested on long-term, continuous data to further assess their success.

Item Type:	Journal article
Publication Title:	Computers and Electronics in Agriculture
Creators:	Hall, H., Bencsik, M., Capela, N., Sousa, J.P. and de Graaf, D.C.
Publisher:	Elsevier BV
Date:	August 2025
Volume:	235
ISSN:	0168-1699
Identifiers:	Number Type 10.1016/j.compag.2025.110307 DOI S0168169925004132 Publisher Item Identifier 2419688 Other
Rights:	© 2025 The Authors. Published by Elsevier B.V. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Divisions:	Schools > School of Science and Technology
Record created by:	Melissa Cornwell
Date Added:	01 Apr 2025 14:22
Last Modified:	01 Apr 2025 14:22
URI:	https://irep.ntu.ac.uk/id/eprint/53340

Actions (login required)

Edit View

Statistics

Views

Downloads