Aerial images to monitor grapevine vegetative growth

Detalhes bibliográficos
Autor(a) principal: Pereira, Janielle Souza
Data de Publicação: 2022
Outros Autores: Ferraz, Gabriel Araújo e Silva, Santana, Lucas Santos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Engenharia na Agricultura
Texto Completo: https://periodicos.ufv.br/reveng/article/view/13390
Resumo: Images obtained by Remotely Piloted Aircraft (RPA) used to monitor the crop can help evaluate leaf mass, plant formation, and plant population. In this context, the objectives of this study were to analyze plant growth in a grapevine crop trained in the trellis system, detect failures and determine the plant covered area using images obtained by RPA. The flight was parameterized with frontal overlap of 75%, lateral overlap of 70%, Ground Sample Distance (GSD) of 60 m, and flight speed of 5 m.s-1, using a sensor in the visible range. Processed images showed a stand 3% smaller than projected, an area covered by vine branches occupying 60.8%, undergrowth and invasives represented 5.3%, and exposed soil 33.9%. Vines were identified in the vegetation indices as green points, invasive plants as yellow points, and exposed soil as red points. Image processing obtained with RPA allowed identification of plants in various stages of development, with predominance of vines in the formation process. It was possible to identify the plants and quantify the leaf mass using the MPRI vegetation index, as well as to differentiate exposed soil from plant material. It was also observed that the plot had an incomplete stand at the time the flight was performed.
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spelling Aerial images to monitor grapevine vegetative growthAerial images to monitor grapevine vegetative growthPrecision agriculturePlant coverPlanting faultsGrapePrecision agriculturePlant coverPlanting failureGrapeImages obtained by Remotely Piloted Aircraft (RPA) used to monitor the crop can help evaluate leaf mass, plant formation, and plant population. In this context, the objectives of this study were to analyze plant growth in a grapevine crop trained in the trellis system, detect failures and determine the plant covered area using images obtained by RPA. The flight was parameterized with frontal overlap of 75%, lateral overlap of 70%, Ground Sample Distance (GSD) of 60 m, and flight speed of 5 m.s-1, using a sensor in the visible range. Processed images showed a stand 3% smaller than projected, an area covered by vine branches occupying 60.8%, undergrowth and invasives represented 5.3%, and exposed soil 33.9%. Vines were identified in the vegetation indices as green points, invasive plants as yellow points, and exposed soil as red points. Image processing obtained with RPA allowed identification of plants in various stages of development, with predominance of vines in the formation process. It was possible to identify the plants and quantify the leaf mass using the MPRI vegetation index, as well as to differentiate exposed soil from plant material. It was also observed that the plot had an incomplete stand at the time the flight was performed.Images obtained by Remotely Piloted Aircraft (RPA) used to monitor the crop can help evaluate leaf mass, plant formation, and plant population. In this context, the objectives of this study were to analyze plant growth in a grapevine crop trained in the trellis system, detect failures and determine the plant covered area using images obtained by RPA. The flight was parameterized with frontal overlap of 75%, lateral overlap of 70%, Ground Sample Distance (GSD) of 60 m, and flight speed of 5 m.s-1, using a sensor in the visible range. Processed images showed a stand 3% smaller than projected, an area covered by vine branches occupying 60.8%, undergrowth and invasives represented 5.3%, and exposed soil 33.9%. Vines were identified in the vegetation indices as green points, invasive plants as yellow points, and exposed soil as red points. Image processing obtained with RPA allowed identification of plants in various stages of development, with predominance of vines in the formation process. It was possible to identify the plants and quantify the leaf mass using the MPRI vegetation index, as well as to differentiate exposed soil from plant material. It was also observed that the plot had an incomplete stand at the time the flight was performed.Universidade Federal de Viçosa - UFV2022-06-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufv.br/reveng/article/view/1339010.13083/reveng.v30i1.13390Engineering in Agriculture; Vol. 30 No. Contínua (2022); 166-174Revista Engenharia na Agricultura - REVENG; v. 30 n. Contínua (2022); 166-1742175-68131414-3984reponame:Engenharia na Agriculturainstname:Universidade Federal de Viçosa (UFV)instacron:UFVenghttps://periodicos.ufv.br/reveng/article/view/13390/7370Copyright (c) 2022 Revista Engenharia na Agricultura - REVENGhttps://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessPereira, Janielle SouzaFerraz, Gabriel Araújo e SilvaSantana, Lucas Santos2023-01-23T14:06:10Zoai:ojs.periodicos.ufv.br:article/13390Revistahttps://periodicos.ufv.br/revengPUBhttps://periodicos.ufv.br/reveng/oairevistaengenharianagricultura@gmail.com||andrerosa@ufv.br||tramitacao.reveng@gmail.com|| reveng@ufv.br2175-68131414-3984opendoar:2023-01-23T14:06:10Engenharia na Agricultura - Universidade Federal de Viçosa (UFV)false
dc.title.none.fl_str_mv Aerial images to monitor grapevine vegetative growth
Aerial images to monitor grapevine vegetative growth
title Aerial images to monitor grapevine vegetative growth
spellingShingle Aerial images to monitor grapevine vegetative growth
Pereira, Janielle Souza
Precision agriculture
Plant cover
Planting faults
Grape
Precision agriculture
Plant cover
Planting failure
Grape
title_short Aerial images to monitor grapevine vegetative growth
title_full Aerial images to monitor grapevine vegetative growth
title_fullStr Aerial images to monitor grapevine vegetative growth
title_full_unstemmed Aerial images to monitor grapevine vegetative growth
title_sort Aerial images to monitor grapevine vegetative growth
author Pereira, Janielle Souza
author_facet Pereira, Janielle Souza
Ferraz, Gabriel Araújo e Silva
Santana, Lucas Santos
author_role author
author2 Ferraz, Gabriel Araújo e Silva
Santana, Lucas Santos
author2_role author
author
dc.contributor.author.fl_str_mv Pereira, Janielle Souza
Ferraz, Gabriel Araújo e Silva
Santana, Lucas Santos
dc.subject.por.fl_str_mv Precision agriculture
Plant cover
Planting faults
Grape
Precision agriculture
Plant cover
Planting failure
Grape
topic Precision agriculture
Plant cover
Planting faults
Grape
Precision agriculture
Plant cover
Planting failure
Grape
description Images obtained by Remotely Piloted Aircraft (RPA) used to monitor the crop can help evaluate leaf mass, plant formation, and plant population. In this context, the objectives of this study were to analyze plant growth in a grapevine crop trained in the trellis system, detect failures and determine the plant covered area using images obtained by RPA. The flight was parameterized with frontal overlap of 75%, lateral overlap of 70%, Ground Sample Distance (GSD) of 60 m, and flight speed of 5 m.s-1, using a sensor in the visible range. Processed images showed a stand 3% smaller than projected, an area covered by vine branches occupying 60.8%, undergrowth and invasives represented 5.3%, and exposed soil 33.9%. Vines were identified in the vegetation indices as green points, invasive plants as yellow points, and exposed soil as red points. Image processing obtained with RPA allowed identification of plants in various stages of development, with predominance of vines in the formation process. It was possible to identify the plants and quantify the leaf mass using the MPRI vegetation index, as well as to differentiate exposed soil from plant material. It was also observed that the plot had an incomplete stand at the time the flight was performed.
publishDate 2022
dc.date.none.fl_str_mv 2022-06-20
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://periodicos.ufv.br/reveng/article/view/13390
10.13083/reveng.v30i1.13390
url https://periodicos.ufv.br/reveng/article/view/13390
identifier_str_mv 10.13083/reveng.v30i1.13390
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://periodicos.ufv.br/reveng/article/view/13390/7370
dc.rights.driver.fl_str_mv Copyright (c) 2022 Revista Engenharia na Agricultura - REVENG
https://creativecommons.org/licenses/by-nc/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2022 Revista Engenharia na Agricultura - REVENG
https://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Viçosa - UFV
publisher.none.fl_str_mv Universidade Federal de Viçosa - UFV
dc.source.none.fl_str_mv Engineering in Agriculture; Vol. 30 No. Contínua (2022); 166-174
Revista Engenharia na Agricultura - REVENG; v. 30 n. Contínua (2022); 166-174
2175-6813
1414-3984
reponame:Engenharia na Agricultura
instname:Universidade Federal de Viçosa (UFV)
instacron:UFV
instname_str Universidade Federal de Viçosa (UFV)
instacron_str UFV
institution UFV
reponame_str Engenharia na Agricultura
collection Engenharia na Agricultura
repository.name.fl_str_mv Engenharia na Agricultura - Universidade Federal de Viçosa (UFV)
repository.mail.fl_str_mv revistaengenharianagricultura@gmail.com||andrerosa@ufv.br||tramitacao.reveng@gmail.com|| reveng@ufv.br
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