Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images

Detalhes bibliográficos
Autor(a) principal: Sulemane, Shazia
Data de Publicação: 2022
Outros Autores: Matos-Carvalho, João P., Pedro, Dário, Moutinho, Filipe, Correia, Sérgio D.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/148294
Resumo: info:eu-repo/grantAgreement/FCT/Investigador FCT/IF%2F00325%2F2015%2FCP1275%2FCT0001/PT Publisher Copyright: © 2022 by the authors.
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spelling Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Imagesartificial intelligenceconvolutional neural networksimage processingmulti-spectral visionprecision agriculturesemantic segmentationunmanned aerial vehicleYou Only Look OnceTheoretical Computer ScienceNumerical AnalysisComputational Theory and MathematicsComputational Mathematicsinfo:eu-repo/grantAgreement/FCT/Investigador FCT/IF%2F00325%2F2015%2FCP1275%2FCT0001/PT Publisher Copyright: © 2022 by the authors.This paper focuses on the gaps that occur inside plantations; these gaps, although not having anything growing in them, still happen to be watered. This action ends up wasting tons of liters of water every year, which translates into financial and environmental losses. To avoid these losses, we suggest early detection. To this end, we analyzed the different available neural networks available with multispectral images. This entailed training each regional and regression-based network five times with five different datasets. Networks based on two possible solutions were chosen: unmanned aerial vehicle (UAV) depletion or post-processing with external software. The results show that the best network for UAV depletion is the Tiny-YOLO (You Only Look Once) version 4-type network, and the best starting weights for Mask-RCNN were from the Tiny-YOLO network version. Although no mean average precision (mAP) of over 70% was achieved, the final trained networks managed to detect mostly gaps, including low-vegetation areas and very small gaps, which had a tendency to be overlooked during the labeling stage.DEE - Departamento de Engenharia Electrotécnica e de ComputadoresUNINOVA-Instituto de Desenvolvimento de Novas TecnologiasCTS - Centro de Tecnologia e SistemasRUNSulemane, ShaziaMatos-Carvalho, João P.Pedro, DárioMoutinho, FilipeCorreia, Sérgio D.2023-01-27T22:20:15Z2022-11-222022-11-22T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article23application/pdfhttp://hdl.handle.net/10362/148294eng1999-4893PURE: 51568228https://doi.org/10.3390/a15120440info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:29:43Zoai:run.unl.pt:10362/148294Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:19.103956Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
title Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
spellingShingle Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
Sulemane, Shazia
artificial intelligence
convolutional neural networks
image processing
multi-spectral vision
precision agriculture
semantic segmentation
unmanned aerial vehicle
You Only Look Once
Theoretical Computer Science
Numerical Analysis
Computational Theory and Mathematics
Computational Mathematics
title_short Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
title_full Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
title_fullStr Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
title_full_unstemmed Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
title_sort Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
author Sulemane, Shazia
author_facet Sulemane, Shazia
Matos-Carvalho, João P.
Pedro, Dário
Moutinho, Filipe
Correia, Sérgio D.
author_role author
author2 Matos-Carvalho, João P.
Pedro, Dário
Moutinho, Filipe
Correia, Sérgio D.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv DEE - Departamento de Engenharia Electrotécnica e de Computadores
UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
CTS - Centro de Tecnologia e Sistemas
RUN
dc.contributor.author.fl_str_mv Sulemane, Shazia
Matos-Carvalho, João P.
Pedro, Dário
Moutinho, Filipe
Correia, Sérgio D.
dc.subject.por.fl_str_mv artificial intelligence
convolutional neural networks
image processing
multi-spectral vision
precision agriculture
semantic segmentation
unmanned aerial vehicle
You Only Look Once
Theoretical Computer Science
Numerical Analysis
Computational Theory and Mathematics
Computational Mathematics
topic artificial intelligence
convolutional neural networks
image processing
multi-spectral vision
precision agriculture
semantic segmentation
unmanned aerial vehicle
You Only Look Once
Theoretical Computer Science
Numerical Analysis
Computational Theory and Mathematics
Computational Mathematics
description info:eu-repo/grantAgreement/FCT/Investigador FCT/IF%2F00325%2F2015%2FCP1275%2FCT0001/PT Publisher Copyright: © 2022 by the authors.
publishDate 2022
dc.date.none.fl_str_mv 2022-11-22
2022-11-22T00:00:00Z
2023-01-27T22:20:15Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/148294
url http://hdl.handle.net/10362/148294
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1999-4893
PURE: 51568228
https://doi.org/10.3390/a15120440
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 23
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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