Supervised classification and NDVI calculation from remote piloted aircraft images for coffee plantations applications
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/50889 |
Resumo: | A tool that has been widely used in Precision Agriculture (PA) is the Remote Piloted Aircraft’s (RPA’s). These tools are used to monitor crops, in addition to checking and quantifying various attributes related to plants. However, there are few studies that evaluate the applicability of this technology in coffee plantations. The objective of this study is to present the applicability of two tools associated with PA and remote sensing to monitoring a coffee planta-tion. The study was conducted in the municipality of Três Pontas, Brazil, comprised a 1.2 ha coffee plantation. Data were collected during a flight with an eBee SQ RPA, and high spatial resolution images were captured by a Parrot Sequoia multispectral sensor coupled to the aircraft. The images were processed using the software Pix4D, thus creating an orthomosaic that was later uploaded to QGIS software. In this program, a supervised classification of land use and land cover was performed using the maximum likelihood method, and the following classes were obtained: coffee plant, exposed soil, and undergrowth. From the mapping accuracy, an overall accuracy and kappa index of 91% and 85% were obtained, respectively. In addition to the supervised classification of the site, the normalized difference vegetation index (NDVI) was calculated for only the coffee plant class. The NDVI map showed the areas of the plantation coffee crop with higher and lower vegetative vigour. |
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Supervised classification and NDVI calculation from remote piloted aircraft images for coffee plantations applicationsMappingPrecision coffee farmingRemotely piloted aircraftA tool that has been widely used in Precision Agriculture (PA) is the Remote Piloted Aircraft’s (RPA’s). These tools are used to monitor crops, in addition to checking and quantifying various attributes related to plants. However, there are few studies that evaluate the applicability of this technology in coffee plantations. The objective of this study is to present the applicability of two tools associated with PA and remote sensing to monitoring a coffee planta-tion. The study was conducted in the municipality of Três Pontas, Brazil, comprised a 1.2 ha coffee plantation. Data were collected during a flight with an eBee SQ RPA, and high spatial resolution images were captured by a Parrot Sequoia multispectral sensor coupled to the aircraft. The images were processed using the software Pix4D, thus creating an orthomosaic that was later uploaded to QGIS software. In this program, a supervised classification of land use and land cover was performed using the maximum likelihood method, and the following classes were obtained: coffee plant, exposed soil, and undergrowth. From the mapping accuracy, an overall accuracy and kappa index of 91% and 85% were obtained, respectively. In addition to the supervised classification of the site, the normalized difference vegetation index (NDVI) was calculated for only the coffee plant class. The NDVI map showed the areas of the plantation coffee crop with higher and lower vegetative vigour.Universidade Federal de Lavras2022-08-08T19:00:21Z2022-08-08T19:00:21Z2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfSANTOS, S. A. dos et al. Supervised classification and NDVI calculation from remote piloted aircraft images for coffee plantations applications. Coffee Science, Lavras, v. 16, 2021.http://repositorio.ufla.br/jspui/handle/1/50889Coffee Sciencereponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessSantos, Sthéfany Airane dosFerraz, Gabriel Araújo e SilvaFigueiredo, Vanessa CastroSantana, Lucas SantosCampos, Beatriz Fonseca Dominikeng2023-05-03T11:52:57Zoai:localhost:1/50889Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-03T11:52:57Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Supervised classification and NDVI calculation from remote piloted aircraft images for coffee plantations applications |
title |
Supervised classification and NDVI calculation from remote piloted aircraft images for coffee plantations applications |
spellingShingle |
Supervised classification and NDVI calculation from remote piloted aircraft images for coffee plantations applications Santos, Sthéfany Airane dos Mapping Precision coffee farming Remotely piloted aircraft |
title_short |
Supervised classification and NDVI calculation from remote piloted aircraft images for coffee plantations applications |
title_full |
Supervised classification and NDVI calculation from remote piloted aircraft images for coffee plantations applications |
title_fullStr |
Supervised classification and NDVI calculation from remote piloted aircraft images for coffee plantations applications |
title_full_unstemmed |
Supervised classification and NDVI calculation from remote piloted aircraft images for coffee plantations applications |
title_sort |
Supervised classification and NDVI calculation from remote piloted aircraft images for coffee plantations applications |
author |
Santos, Sthéfany Airane dos |
author_facet |
Santos, Sthéfany Airane dos Ferraz, Gabriel Araújo e Silva Figueiredo, Vanessa Castro Santana, Lucas Santos Campos, Beatriz Fonseca Dominik |
author_role |
author |
author2 |
Ferraz, Gabriel Araújo e Silva Figueiredo, Vanessa Castro Santana, Lucas Santos Campos, Beatriz Fonseca Dominik |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Santos, Sthéfany Airane dos Ferraz, Gabriel Araújo e Silva Figueiredo, Vanessa Castro Santana, Lucas Santos Campos, Beatriz Fonseca Dominik |
dc.subject.por.fl_str_mv |
Mapping Precision coffee farming Remotely piloted aircraft |
topic |
Mapping Precision coffee farming Remotely piloted aircraft |
description |
A tool that has been widely used in Precision Agriculture (PA) is the Remote Piloted Aircraft’s (RPA’s). These tools are used to monitor crops, in addition to checking and quantifying various attributes related to plants. However, there are few studies that evaluate the applicability of this technology in coffee plantations. The objective of this study is to present the applicability of two tools associated with PA and remote sensing to monitoring a coffee planta-tion. The study was conducted in the municipality of Três Pontas, Brazil, comprised a 1.2 ha coffee plantation. Data were collected during a flight with an eBee SQ RPA, and high spatial resolution images were captured by a Parrot Sequoia multispectral sensor coupled to the aircraft. The images were processed using the software Pix4D, thus creating an orthomosaic that was later uploaded to QGIS software. In this program, a supervised classification of land use and land cover was performed using the maximum likelihood method, and the following classes were obtained: coffee plant, exposed soil, and undergrowth. From the mapping accuracy, an overall accuracy and kappa index of 91% and 85% were obtained, respectively. In addition to the supervised classification of the site, the normalized difference vegetation index (NDVI) was calculated for only the coffee plant class. The NDVI map showed the areas of the plantation coffee crop with higher and lower vegetative vigour. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2022-08-08T19:00:21Z 2022-08-08T19:00:21Z |
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 |
SANTOS, S. A. dos et al. Supervised classification and NDVI calculation from remote piloted aircraft images for coffee plantations applications. Coffee Science, Lavras, v. 16, 2021. http://repositorio.ufla.br/jspui/handle/1/50889 |
identifier_str_mv |
SANTOS, S. A. dos et al. Supervised classification and NDVI calculation from remote piloted aircraft images for coffee plantations applications. Coffee Science, Lavras, v. 16, 2021. |
url |
http://repositorio.ufla.br/jspui/handle/1/50889 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Lavras |
publisher.none.fl_str_mv |
Universidade Federal de Lavras |
dc.source.none.fl_str_mv |
Coffee Science reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1807835221580578816 |