Remotely piloted aircraft and random forest in the evaluation of the spatial variability of foliar nitrogen in coffee crop

Detalhes bibliográficos
Autor(a) principal: Marin, Diego Bedin
Data de Publicação: 2021
Outros Autores: Ferraz, Gabriel Araújo e Silva, Guimarães, Paulo Henrique Sales, Schwerz, Felipe, Santana, Lucas Santos, Barbosa, Brenon Dienevam Souza, Barata, Rafael Alexandre Pena, Faria, Rafael de Oliveira, Dias, Jessica Ellen Lima, Conti, Leonardo, Rossi, Giuseppe
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/48975
Resumo: The development of approaches to determine the spatial variability of nitrogen (N) into coffee leaves is essential to increase productivity and reduce production costs and environmental impacts associated with excessive N applications. Thus, this study aimed to assess the potential of the Random Forest (RF) machine learning method applied to vegetation indices (VI) obtained from Remotely Piloted Aircraft (RPA) images to measure the N content in coffee plants. A total of 10 VI were obtained from multispectral images by a camera attached to a rotary-wing RPA. The RGB orthomosaic was used to determine sampling points at the crop area, which were ranked by N levels in the plants as deficient, critical, or sufficient. The chemical analysis of N content in the coffee leaves, as well as the VI values in sample points, were used as input parameters for the image training and its classification by the RF. The suggested model has shown global accuracy and a kappa coefficient of up to 0.91 and 0.86, respectively. The best results were achieved using the Green Normalized Difference Vegetation (GNDVI) and Green Optimized Soil Adjusted Vegetation Index (GOSAVI). In addition, the model enabled the evaluation of the spatial distribution of N in the coffee trees, as well as quantification of N deficiency in the crop for the whole area. The GNDVI and GOSAVI allowed the verification that 22% of the entire crop area had plants with N deficiency symptoms, which would result in a reduction of 78% in the amount of N applied by the producer.
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spelling Remotely piloted aircraft and random forest in the evaluation of the spatial variability of foliar nitrogen in coffee cropMachine learningVegetation indicesUnmanned aerial vehicleNitrogen managementRGB cameraThe development of approaches to determine the spatial variability of nitrogen (N) into coffee leaves is essential to increase productivity and reduce production costs and environmental impacts associated with excessive N applications. Thus, this study aimed to assess the potential of the Random Forest (RF) machine learning method applied to vegetation indices (VI) obtained from Remotely Piloted Aircraft (RPA) images to measure the N content in coffee plants. A total of 10 VI were obtained from multispectral images by a camera attached to a rotary-wing RPA. The RGB orthomosaic was used to determine sampling points at the crop area, which were ranked by N levels in the plants as deficient, critical, or sufficient. The chemical analysis of N content in the coffee leaves, as well as the VI values in sample points, were used as input parameters for the image training and its classification by the RF. The suggested model has shown global accuracy and a kappa coefficient of up to 0.91 and 0.86, respectively. The best results were achieved using the Green Normalized Difference Vegetation (GNDVI) and Green Optimized Soil Adjusted Vegetation Index (GOSAVI). In addition, the model enabled the evaluation of the spatial distribution of N in the coffee trees, as well as quantification of N deficiency in the crop for the whole area. The GNDVI and GOSAVI allowed the verification that 22% of the entire crop area had plants with N deficiency symptoms, which would result in a reduction of 78% in the amount of N applied by the producer.Multidisciplinary Digital Publishing Institute (MDPI)2022-01-22T02:13:04Z2022-01-22T02:13:04Z2021-04-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfMARIN, D. B. et al. Remotely piloted aircraft and random forest in the evaluation of the spatial variability of foliar nitrogen in coffee crop. Remote Sensing, [S.l.], v. 13, n. 8, p. 1-15, Apr. 2021. DOI: 10.3390/rs13081471.http://repositorio.ufla.br/jspui/handle/1/48975Remote Sensingreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessMarin, Diego BedinFerraz, Gabriel Araújo e SilvaGuimarães, Paulo Henrique SalesSchwerz, FelipeSantana, Lucas SantosBarbosa, Brenon Dienevam SouzaBarata, Rafael Alexandre PenaFaria, Rafael de OliveiraDias, Jessica Ellen LimaConti, LeonardoRossi, Giuseppeeng2023-05-03T11:38:21Zoai:localhost:1/48975Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-03T11:38:21Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Remotely piloted aircraft and random forest in the evaluation of the spatial variability of foliar nitrogen in coffee crop
title Remotely piloted aircraft and random forest in the evaluation of the spatial variability of foliar nitrogen in coffee crop
spellingShingle Remotely piloted aircraft and random forest in the evaluation of the spatial variability of foliar nitrogen in coffee crop
Marin, Diego Bedin
Machine learning
Vegetation indices
Unmanned aerial vehicle
Nitrogen management
RGB camera
title_short Remotely piloted aircraft and random forest in the evaluation of the spatial variability of foliar nitrogen in coffee crop
title_full Remotely piloted aircraft and random forest in the evaluation of the spatial variability of foliar nitrogen in coffee crop
title_fullStr Remotely piloted aircraft and random forest in the evaluation of the spatial variability of foliar nitrogen in coffee crop
title_full_unstemmed Remotely piloted aircraft and random forest in the evaluation of the spatial variability of foliar nitrogen in coffee crop
title_sort Remotely piloted aircraft and random forest in the evaluation of the spatial variability of foliar nitrogen in coffee crop
author Marin, Diego Bedin
author_facet Marin, Diego Bedin
Ferraz, Gabriel Araújo e Silva
Guimarães, Paulo Henrique Sales
Schwerz, Felipe
Santana, Lucas Santos
Barbosa, Brenon Dienevam Souza
Barata, Rafael Alexandre Pena
Faria, Rafael de Oliveira
Dias, Jessica Ellen Lima
Conti, Leonardo
Rossi, Giuseppe
author_role author
author2 Ferraz, Gabriel Araújo e Silva
Guimarães, Paulo Henrique Sales
Schwerz, Felipe
Santana, Lucas Santos
Barbosa, Brenon Dienevam Souza
Barata, Rafael Alexandre Pena
Faria, Rafael de Oliveira
Dias, Jessica Ellen Lima
Conti, Leonardo
Rossi, Giuseppe
author2_role author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Marin, Diego Bedin
Ferraz, Gabriel Araújo e Silva
Guimarães, Paulo Henrique Sales
Schwerz, Felipe
Santana, Lucas Santos
Barbosa, Brenon Dienevam Souza
Barata, Rafael Alexandre Pena
Faria, Rafael de Oliveira
Dias, Jessica Ellen Lima
Conti, Leonardo
Rossi, Giuseppe
dc.subject.por.fl_str_mv Machine learning
Vegetation indices
Unmanned aerial vehicle
Nitrogen management
RGB camera
topic Machine learning
Vegetation indices
Unmanned aerial vehicle
Nitrogen management
RGB camera
description The development of approaches to determine the spatial variability of nitrogen (N) into coffee leaves is essential to increase productivity and reduce production costs and environmental impacts associated with excessive N applications. Thus, this study aimed to assess the potential of the Random Forest (RF) machine learning method applied to vegetation indices (VI) obtained from Remotely Piloted Aircraft (RPA) images to measure the N content in coffee plants. A total of 10 VI were obtained from multispectral images by a camera attached to a rotary-wing RPA. The RGB orthomosaic was used to determine sampling points at the crop area, which were ranked by N levels in the plants as deficient, critical, or sufficient. The chemical analysis of N content in the coffee leaves, as well as the VI values in sample points, were used as input parameters for the image training and its classification by the RF. The suggested model has shown global accuracy and a kappa coefficient of up to 0.91 and 0.86, respectively. The best results were achieved using the Green Normalized Difference Vegetation (GNDVI) and Green Optimized Soil Adjusted Vegetation Index (GOSAVI). In addition, the model enabled the evaluation of the spatial distribution of N in the coffee trees, as well as quantification of N deficiency in the crop for the whole area. The GNDVI and GOSAVI allowed the verification that 22% of the entire crop area had plants with N deficiency symptoms, which would result in a reduction of 78% in the amount of N applied by the producer.
publishDate 2021
dc.date.none.fl_str_mv 2021-04-10
2022-01-22T02:13:04Z
2022-01-22T02:13:04Z
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 MARIN, D. B. et al. Remotely piloted aircraft and random forest in the evaluation of the spatial variability of foliar nitrogen in coffee crop. Remote Sensing, [S.l.], v. 13, n. 8, p. 1-15, Apr. 2021. DOI: 10.3390/rs13081471.
http://repositorio.ufla.br/jspui/handle/1/48975
identifier_str_mv MARIN, D. B. et al. Remotely piloted aircraft and random forest in the evaluation of the spatial variability of foliar nitrogen in coffee crop. Remote Sensing, [S.l.], v. 13, n. 8, p. 1-15, Apr. 2021. DOI: 10.3390/rs13081471.
url http://repositorio.ufla.br/jspui/handle/1/48975
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 Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.none.fl_str_mv Remote Sensing
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
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