Aerial images to monitor grapevine vegetative growth
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
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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Engenharia na Agricultura |
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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 |
_version_ |
1800211147337498624 |