Remotely piloted aircraft and random forest in the evaluation of the spatial variability of foliar nitrogen in coffee crop
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/1/48975 |
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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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 |
_version_ |
1815439290821246976 |