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Pest detection in olive groves using YOLOv7 and YOLOv8 models

2024-02-03Documento de conferência Acesso aberto
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorAlves, Adília
dc.contributor.authorPereira, José Alberto Cardoso
dc.contributor.authorKhanal, Salik
dc.contributor.authorMorais, A. Jorge
dc.contributor.authorFilipe, Vítor Manuel Jesus
dc.contributor.editorPereira, Ana I.
dc.contributor.editorMendes, Armando
dc.contributor.editorFernandes, Florbela P.
dc.contributor.editorPacheco, Maria F.
dc.contributor.editorCoelho, João P.
dc.contributor.editorLima, José
dc.date.accessioned2026-02-26T14:42:25Z
dc.date.available2026-02-26T14:42:25Z
dc.date.issued2024-02-03
dc.description.abstractModern agriculture faces important challenges for feeding a fast-growing planet’s population in a sustainable way. One of the most important challenges faced by agriculture is the increasing destruction caused by pests to important crops. It is very important to control and manage pests in order to reduce the losses they cause. However, pest detection and monitoring are very resources consuming tasks. The recent development of computer vision-based technology has made it possible to automatize pest detection efficiently. In Mediterranean olive groves, the olive fly (Bactrocera oleae Rossi) is considered the key-pest of the crop. This paper presents olive fly detection using the lightweight YOLO-based model for versions 7 and 8, respectively, YOLOv7-tiny and YOLOv8n. The proposed object detection models were trained, validated, and tested using two different image datasets collected in various locations of Portugal and Greece. The images are constituted by sticky yellow trap photos and by McPhail trap photos with olive fly exemplars. The performance of the models was evaluated using precision, recall, and mAP.95. The YOLOV7-tiny model best performance is 88.3% of precision, 85% of Recall, 90% of mAP.50, and 53% of mAP.95. The YOLOV8n model best performance is 85% of precision, 85% of Recall, 90% mAP.50, and 55% of mAP.50 YOLO8n model achieved worst results than YOLOv7-tiny for a dataset without negative images (images without olive fly exemplars). Aiming at installing an experimental prototype in the olive grove, the YOLOv8n model was implemented in a Ubuntu Server 23.04 Raspberry PI 3 microcomputer.eng
dc.description.sponsorshipNational funds FCT/MCTES (PIDDAC) to CIMO (UIDB/00690/2020 and UIDP/00690/2020) and SusTEC (LA/P/0007/2020).
dc.identifier.authenticusidP-00Z-WX3
dc.identifier.citationAlves, A., Pereira, J., Khanal, S., Morais, A.J., Filipe, V. (2024). Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models. In: Pereira, A.I., Mendes, A., Fernandes, F.P., Pacheco, M.F., Coelho, J.P., Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1982 . Springer, Cham. https://doi.org/10.1007/978-3-031-53036-4_4
dc.identifier.doi10.1007/978-3-031-53036-4_4
dc.identifier.eid2-s2.0-85185824413
dc.identifier.urihttp://hdl.handle.net/10400.2/21541
dc.identifier.wos001264686300004
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer, Cham
dc.relationMountain Research Center
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-031-53036-4_4
dc.relation.ispartofseriesCommunications in Computer and Information Science
dc.rights.uriN/A
dc.subjectOlives sustainable production
dc.subjectConvolutional neural network
dc.subjectDeep learning
dc.subjectYOLOv7
dc.subjectYOLOv8
dc.titlePest detection in olive groves using YOLOv7 and YOLOv8 modelseng
dc.typeconference object
dspace.entity.typePublication
oaire.awardNumberUIDB/00690/2020
oaire.awardNumberLA/P/0007/2020
oaire.awardTitleMountain Research Center
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIhttp://hdl.handle.net/10400.2/21538
oaire.awardURIhttp://hdl.handle.net/10400.2/21539
oaire.citation.conferenceDate2023-09
oaire.citation.conferencePlacePonta Delgada, Açores, Portugal
oaire.citation.endPage62
oaire.citation.startPage50
oaire.citation.titleOptimization, Learning Algorithms and Applications (OL2A 2023)
oaire.citation.volume1982
oaire.fundingStreamConcurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base
oaire.fundingStreamConcurso para Atribuição do Estatuto e Financiamento de Laboratórios Associados (LA)
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa
person.familyNameAlves
person.familyNamePereira
person.familyNameKhanal
person.familyNameMorais
person.familyNameFilipe
person.givenNameAdília
person.givenNameJosé Alberto Cardoso
person.givenNameSalik
person.givenNameA. Jorge
person.givenNameVítor Manuel Jesus
person.identifierD-1723-2009
person.identifier.ciencia-id611F-80B2-A7C1
person.identifier.ciencia-idF314-1D77-536E
person.identifier.ciencia-idE716-23C3-FAFF
person.identifier.orcid0000-0002-3792-1968
person.identifier.orcid0000-0002-2260-0600
person.identifier.orcid0000-0003-0538-3699
person.identifier.orcid0000-0003-2224-1609
person.identifier.orcid0000-0002-3747-6577
person.identifier.scopus-author-id57194584599
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