A novel resource-constrained insect monitoring system based on machine vision with edge AI

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dc.contributor.authorKargar, Amin
dc.contributor.authorWilk, Mariusz P.
dc.contributor.authorZorbas, Dimitrios
dc.contributor.authorGaffney, Michael T.
dc.contributor.authorO'Flynn, Brendan
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.contributor.funderDepartment of Agriculture, Food and the Marine, Irelanden
dc.contributor.funderTeagascen
dc.date.accessioned2023-03-01T16:37:56Z
dc.date.available2023-03-01T16:37:56Z
dc.date.issued2023-02-28
dc.description.abstractEffective insect pest monitoring is a vital component of Integrated Pest Management (IPM) strategies. It helps to support crop productivity while minimising the need for plant protection products. In recent years, many researchers have considered the integration of intelligence into such systems in the context of the Smart Agriculture research agenda. This paper describes the development of a smart pest monitoring system, developed in accordance with specific requirements associated with the agricultural sector. The proposed system is a low-cost smart insect trap, for use in orchards, that detects specific insect species that are detrimental to fruit quality. The system helps to identify the invasive insect, Brown Marmorated Stink Bug (BMSB) or Halyomorpha halys (HH) using a Microcontroller Unit-based edge device comprising of an Internet of Things enabled, resource-constrained image acquisition and processing system. It is used to execute our proposed lightweight image analysis algorithm and Convolutional Neural Network (CNN) model for insect detection and classification, respectively. The prototype device is currently deployed in an orchard in Italy. The preliminary experimental results show over 70 percent of accuracy in BMSB classification on our custom-built dataset, demonstrating the proposed system feasibility and effectiveness in monitoring this invasive insect species.en
dc.description.sponsorshipEuropean Regional Development Fund (Ireland’s European Structural and Investment Funds Programmes 2014–2020); Department of Agriculture, Food and the Marine (Grant: 2020 Trans National ERANET); Teagasc (Walsh Scholarship); Science Foundation Ireland (Grant 12/RC/2289-P2-INSIGHT)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationKargar, A., Wilk, M. P., Zorbas, D., Gaffney, M. T. and O'Flynn, B. (2023) 'A novel resource-constrained insect monitoring system based on machine vision with edge AI', 2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS), Genova, Italy, 5 - 7 December, pp. 1-6. doi: 10.1109/IPAS55744.2022.10052895en
dc.identifier.doi10.1109/IPAS55744.2022.10052895en
dc.identifier.endpage6en
dc.identifier.isbn978-1-6654-6219-8
dc.identifier.isbn978-1-6654-6220-4
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/14271
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)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© 2023, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectIntegrated Pest Management (IPM)en
dc.subjectMachine visionen
dc.subjectImage processingen
dc.subjectDeep learningen
dc.subjectEdge AIen
dc.subjectIntegrated pest monitoringen
dc.subjectFood securityen
dc.titleA novel resource-constrained insect monitoring system based on machine vision with edge AIen
dc.typeConference itemen
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