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dc.contributor.authorGarcía Navarrete, Óscar Leonardo
dc.contributor.authorSantamaría Becerril, Óscar
dc.contributor.authorMartín Ramos, Pablo
dc.contributor.authorValenzuela Mahecha, Miguel Ángel
dc.contributor.authorNavas Gracia, Luis Manuel 
dc.date.accessioned2024-06-10T11:03:44Z
dc.date.available2024-06-10T11:03:44Z
dc.date.issued2024
dc.identifier.citationAgriculture, 2024, Vol. 14, Nº. 2, 286es
dc.identifier.issn2077-0472es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/68064
dc.descriptionProducción Científicaes
dc.description.abstractCorn (Zea mays L.) is one of the most important cereals worldwide. To maintain crop productivity, it is important to eliminate weeds that compete for nutrients and other resources. The eradication of these causes environmental problems through the use of agrochemicals. The implementation of technology to mitigate this impact is also a challenge. In this work, an artificial vision system was implemented based on the YOLOv5s (You Only Look Once) model, which uses a single convolutional neural network (CNN) that allows differentiating corn from four types of weeds, for which a mobile support structure was built to capture images. The performance of the trained model had a value of mAP@05 (mean Average Precision) at a threshold of 0.5 of 83.6%. A prediction accuracy of 97% and a mAP@05 of 97.5% were obtained for the maize class. For the weed classes, Lolium perenne, Sonchus oleraceus, Solanum nigrum, and Poa annua obtained an accuracy of 86%, 90%, 78%, and 74%, and a mAP@05 of 81.5%, 90.2%, 76.6% and 72.0%, respectively. The results are encouraging for the construction of a precision weeding system.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherMDPIes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMachine learninges
dc.subjectAprendizaje automáticoes
dc.subjectPrecision agriculturees
dc.subjectAgricultura de precisiónes
dc.subjectSustainable agriculturees
dc.subjectAgricultura sosteniblees
dc.subjectAgricultural innovationses
dc.subjectAgricultura - Innovaciones tecnológicases
dc.subjectNeural networks (Computer science)es
dc.subjectRedes neuronales (Informática)es
dc.subjectComputer visiones
dc.subjectVisión artificial (Robótica)es
dc.subjectAgriculturees
dc.subjectAgricultural engineeringes
dc.titleDevelopment of a detection system for types of weeds in maize (Zea mays L.) under greenhouse conditions using the YOLOv5 v7.0 modeles
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2024 The authorses
dc.identifier.doi10.3390/agriculture14020286es
dc.relation.publisherversionhttps://www.mdpi.com/2077-0472/14/2/286es
dc.identifier.publicationfirstpage286es
dc.identifier.publicationissue2es
dc.identifier.publicationtitleAgriculturees
dc.identifier.publicationvolume14es
dc.peerreviewedSIes
dc.description.projectUnion Europea, European Union’s Horizon 2020 - (grant HORIZON-CL6-2022-FARM2FORK-01)es
dc.identifier.essn2077-0472es
dc.rightsAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones
dc.subject.unesco3102 Ingeniería Agrícolaes
dc.subject.unesco1203.04 Inteligencia Artificiales
dc.subject.unesco1203.17 Informáticaes


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