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Pest Management in Olive Cultivation Through Computer Vision: A Comparative Study of Detection Methods for Yellow Sticky Traps

2024Conference paper Restricted access
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dc.contributor.authorMendes, João
dc.contributor.authorBerger, Guido
dc.contributor.authorLima, José
dc.contributor.authorCosta, Lino
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2025-02-12T16:53:18Z
dc.date.available2025-02-12T16:53:18Z
dc.date.issued2024
dc.description.abstractThis study compares two computer vision methods to detect yellow sticky traps using unmanned autonomous vehicles in olive tree cultivation. The traps aim to combat and monitor the density of the Bactrocera oleae, an important pest that damages olive fruit, leading to substantial economic losses annually. The evaluation encompassed two distinct methods: firstly, an algorithm employing conventional segmentation techniques like thresholding and contour localization, and secondly, a contemporary artificial intelligence approach utilizing YOLOv8, a state-of-the-art technology. A specific dataset was created to train and adjust the two algorithms. At the end of the study, both were able to locate the trap precisely. The segmentation algorithm demonstrated superior performance at proximal distances (50 cm), outperforming the outcomes achieved by YOLOv8. In contrast, YOLOv8 exhibited sustained precision, irrespective of the distance under examination. These findings affirm the versatility of both algorithms, highlighting their adaptability to various contexts based on distinct application demands. Consideration of trade-offs between accuracy and processing speed is essential in determining the most appropriate algorithm for a given application.pt_PT
dc.description.sponsorshipThis work has been supported by: SmartHealth - Inteligência Artificial para Cuidados de Saúde Personalizados ao Longo da Vida, under the project ref. NORTE-01-0145-FEDER-000045; OleaChain: Competências para a sustentabilidade e inovação da cadeia de valor do olival tradicional no Norte Interior de Portugal, NORTE-06-3559-FSE-000188, an operation to hire highly qualified human resources, funded by NORTE 2020 through the European Social Fund (ESF). The authors are grateful to the Foundation for Science and Technology (FCT,Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), ALGORITMI (UIDB/00319/2020) and SusTEC (LA /P/0007/2021). The authors thank Marta Sofia Madureira from the Agrobio Tecnologia - Insects Laboratory, part of the Mountain Research Center (CIMO), for the technical support provided throughout this work.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMendes, João; Berger, Guido; Lima, José; Costa, Lino; Pereira, Ana I. (2024). Pest Management in Olive Cultivation Through Computer Vision: A Comparative Study of Detection Methods for Yellow Sticky Traps. In 6th Iberian Robotics Conference (Robot 2023). Cham: Springer Nature, p. 373-385. ISBN 978-3-031-59166-2.pt_PT
dc.identifier.doi10.1007/978-3-031-59167-9_31pt_PT
dc.identifier.isbn978-3-031-59166-2
dc.identifier.urihttp://hdl.handle.net/10198/31181
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationALGORITMI Research Center
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectUAVspt_PT
dc.subjectComputer visionpt_PT
dc.subjectPrecision agriculturept_PT
dc.subjectPest controlpt_PT
dc.subjectOlive flypt_PT
dc.subjectBactrocera oleaept_PT
dc.subjectYOLOv8pt_PT
dc.subjectSegmentationpt_PT
dc.titlePest Management in Olive Cultivation Through Computer Vision: A Comparative Study of Detection Methods for Yellow Sticky Trapspt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleALGORITMI Research Center
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
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oaire.citation.endPage385pt_PT
oaire.citation.startPage373pt_PT
oaire.citation.title6th Iberian Robotics Conference (Robot 2023)pt_PT
oaire.fundingStream6817 - DCRRNI ID
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oaire.fundingStream6817 - DCRRNI ID
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person.familyNameBerger
person.familyNameLima
person.familyNamePereira
person.givenNameJoão
person.givenNameGuido
person.givenNameJosé
person.givenNameAna I.
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person.identifier.ridL-3370-2014
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project.funder.identifierhttp://doi.org/10.13039/501100001871
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project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
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