Detecting Halyomorpha halys using a low-power edge-based monitoring system

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dc.contributor.authorKargar, Aminen
dc.contributor.authorZorbas, Dimitriosen
dc.contributor.authorTedesco, Salvatoreen
dc.contributor.authorGaffney, Michaelen
dc.contributor.authorO'Flynn, Brendanen
dc.contributor.funderEuropean Regional Development Funden
dc.contributor.funderDepartment of Agriculture, Food and the Marine, Irelanden
dc.contributor.funderTeagascen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2024-06-12T09:54:32Z
dc.date.available2024-06-12T09:54:32Z
dc.date.issued2024-04-25en
dc.description.abstractSmart monitoring systems in orchards can automate agriculture monitoring processes and provide useful information about the presence of insects, such as the Brown Marmorated Stink Bug (BMSB), that threaten the production quantity and quality of fruit such as pears. Unlike other approaches in the literature, we propose a low-cost image monitoring system which exhibits a very low power consumption without compromising much of the accuracy that existing expensive systems incorporating significant computing and processing capability can achieve in such applications. The proposed system relies on a microcontroller unit and a camera which can take pictures of a double-sided sticky insect trap which, with the help of novel machine learning algorithms, can report on the presence of BMSB via a long-range communication link. The Internet of Things data capture and analysis system has recently been deployed in a real orchard in Italy which is subject to BMSB infestation and the first images have been analysed. This paper presents how the system works, the image processing, detection and classification algorithms, as well as a demonstration of the memory and energy consumption associated with the processing algorithms. The system achieves an accuracy of over 90% with multiple times less memory and energy consumption compared to other similar approaches in the literature.en
dc.description.sponsorshipDepartment of Agriculture, Food and the Marine, Ireland (Haly.ID project – 2020EN508 funded by Ireland’s Department of Agriculture, Food and the Marine under Grant: 2020 Trans National ERA-NET); Teagasc, Ireland (Walsh Scholarship); Science Foundation Ireland (Grant 12/RC/2289-P2-INSIGHT, 13/RC/2077-CONNECT, 16/RC/383 5-VISTAMILK and 16/RC/3918-CONFIRM which are co-funded by the European Regional Development Fund (ERDF) under Ireland’s European Structural and Investment Funds Programme 2014–2020)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid108935en
dc.identifier.citationKargar, A., Zorbas, D., Tedesco, S., Gaffney, M. and O’Flynn, B. (2024) ‘Detecting Halyomorpha halys using a low-power edge-based monitoring system’, Computers and Electronics in Agriculture, 221, 108935. Available at: https://doi.org/10.1016/j.compag.2024.108935en
dc.identifier.doihttps://doi.org/10.1016/j.compag.2024.108935en
dc.identifier.endpage9en
dc.identifier.issn0168-1699en
dc.identifier.journaltitleComputers and Electronics in Agricultureen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/15998
dc.identifier.volume221en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.ispartofComputers and Electronics in Agricultureen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 2/12/RC/2289-P2s/IE/INSIGHT Phase 2/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3835/IE/VistaMilk Centre/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.rights© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectHalyomorpha halysen
dc.subjectEdge computingen
dc.subjectMachine learningen
dc.subjectDeep learningen
dc.titleDetecting Halyomorpha halys using a low-power edge-based monitoring systemen
dc.typeArticle (peer-reviewed)en
oaire.citation.volume221en
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