Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/100144
DC FieldValueLanguage
dc.contributor.authorBarros, T.-
dc.contributor.authorConde, P.-
dc.contributor.authorGonçalves, G.-
dc.contributor.authorPremebida, Cristiano-
dc.contributor.authorMonteiro, M.-
dc.contributor.authorFerreira, C. S. S.-
dc.contributor.authorNunes, U. J.-
dc.date.accessioned2022-05-18T11:44:00Z-
dc.date.available2022-05-18T11:44:00Z-
dc.date.issued2022-
dc.identifier.issn01681699-
dc.identifier.urihttps://hdl.handle.net/10316/100144-
dc.description.abstractDigital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural sector, including vineyards which are a relevant crop in the Mediterranean region. In this work, a study is presented of semantic segmentation for vine detection in real-world vineyards by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods. Camera data have been collected on vineyards using an Unmanned Aerial System (UAS) equipped with a dual imaging sensor payload, namely a high-definition RGB camera and a five-band multispectral and thermal camera. Extensive experiments using deep-segmentation networks and unsupervised methods have been performed on multimodal datasets representing four distinct vineyards located in the central region of Portugal. The reported results indicate that SegNet, U-Net, and ModSegNet have equivalent overall performance in vine segmentation. The results also show that multimodality slightly improves the performance of vine segmentation, but the NIR spectrum alone generally is sufficient on most of the datasets. Furthermore, results suggest that high-definition RGB images produce equivalent or higher performance than any lower resolution multispectral band combination. Lastly, Deep Learning (DL) networks have higher overall performance than classical methods. The code and dataset are publicly available on https://github.com/Cybonic/DL_vineyard_segmentation_study.git.pt
dc.language.isoengpt
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/MIT-EXPL/TDI/0029/2019/PT/Intelligent Automation in Precise Agriculture AI+Greenpt
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/781086/EU/Crux Agribotics: The future of cucumbers harvesting - robot for automated agriculture laborspt
dc.relationinfo:eu-repo/grantAgreement/FCT/POR_CENTRO/2021.06492.BD/PT/Multi-modal perception for long-term localization in human-centered roboticspt
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB/00048/2020/PT/INSTITUTE OF SYSTEMS AND ROBOTICS - ISR - COIMBRApt
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB/00308/2020/PT/Institute for Systems Engineering and Computers at Coimbra - INESC Coimbrapt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectMultispectralpt
dc.subjectVineyard segmentationpt
dc.subjectDeep learningpt
dc.subjectPrecision agriculturept
dc.titleMultispectral vineyard segmentation: A deep learning comparison studypt
dc.typearticlept
degois.publication.firstPage106782pt
degois.publication.titleComputers and Electronics in Agriculturept
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0168169922000990pt
dc.peerreviewedyespt
dc.identifier.doi10.1016/j.compag.2022.106782-
degois.publication.volume195pt
dc.date.embargo2022-01-01*
dc.identifier.arxivhttps://doi.org/10.48550/arXiv.2108.01200en
uc.date.periodoEmbargo0pt
item.openairetypearticle-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextCom Texto completo-
crisitem.author.researchunitISR - Institute of Systems and Robotics-
crisitem.author.parentresearchunitUniversity of Coimbra-
crisitem.author.orcid0000-0002-2168-2077-
Appears in Collections:I&D IT - Artigos em Revistas Internacionais
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