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A YOLO-Based Insect Detection: Potential Use of Small Multirotor Unmanned Aerial Vehicles (UAVs) Monitoring

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dc.contributor.authorBerger, Guido
dc.contributor.authorMendes, João
dc.contributor.authorChellal, Arezki Abderrahim
dc.contributor.authorJunior, Luciano Bonzatto
dc.contributor.authorSilva, Yago M.R.
dc.contributor.authorZorawski, Matheus
dc.contributor.authorPereira, Ana I.
dc.contributor.authorPinto, Milena F.
dc.contributor.authorCastro, João Paulo
dc.contributor.authorValente, António
dc.contributor.authorLima, José
dc.date.accessioned2024-10-08T09:48:11Z
dc.date.available2024-10-08T09:48:11Z
dc.date.issued2024
dc.description.abstractThis paper presents an approach to address the challenges of manual inspection using multirotor Unmanned Aerial Vehicles (UAV) to detect olive tree flies (Bactrocera oleae). The study employs computer vision techniques based on the You Only Look Once (YOLO) algorithm to detect insects trapped in yellow chromotropic traps. Therefore, this research evaluates the performance of the YOLOv7 algorithmin detecting and quantify olive tree flies using images obtained from two different digital cameras in a controlled environment at different distances and angles. The findings could potentially contribute to the automation of insect pest inspection by UAV-based robotic systems and highlight potential avenues for future advances in this field. In view of the experiments conducted indoors, it was found that the Arducam IMX477 camera acquires images with greater clarity compared to the TelloCam, making it possible to correctly highlight the set of Bactrocera oleae in different prediction models. The presented results in this research demonstrate that with the introduction of data augmentation and auto label techniques on the set of images of Bactrocera oleae, it was possible to arrive at a prediction model whose average detection was 256 Bactrocera oleae in relation to the corresponding ground truth value to 270 Bactrocera oleae.pt_PT
dc.description.sponsorshipThe 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), SusTEC (LA/P/ 0007/2021), Oleachain “Skills for sustainability and innovation in the value chain of traditional olive groves in the Northern Interior of Portugal” (Norte06-3559-FSE-000188) and Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (CEFET/RJ). 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.citationBerger, Guido S.; Mendes, João; Chellal, Arezki Abderrahim; Junior, Luciano Bonzatto; Silva, Yago M. R. da; Zorawski, Matheus; Pereira, Ana I.; Pinto, Milena F.; Castro, João; Valente, António; Lima, José (2024). A YOLO-Based Insect Detection: Potential Use of Small Multirotor Unmanned Aerial Vehicles (UAVs) Monitoring. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 1, p. 3–17. ISBN 978-3-031-53024-1.pt_PT
dc.identifier.doi10.1007/978-3-031-53025-8_1pt_PT
dc.identifier.isbn978-3-031-53024-1
dc.identifier.isbn978-3-031-53025-8
dc.identifier.urihttp://hdl.handle.net/10198/30351
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.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectUnmanned aerial vehiclespt_PT
dc.subjectObject classificationpt_PT
dc.subjectInsect detectionpt_PT
dc.subjectOlive flypt_PT
dc.subjectYOLOv7 algorithmpt_PT
dc.titleA YOLO-Based Insect Detection: Potential Use of Small Multirotor Unmanned Aerial Vehicles (UAVs) Monitoringpt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardNumberUIDB/05757/2020
oaire.awardNumberUIDP/05757/2020
oaire.awardNumberLA/P/0007/2020
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.endPage17pt_PT
oaire.citation.startPage3pt_PT
oaire.citation.title3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023)pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
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