Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
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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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 |
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1799138123845730304 |