Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements

Detalhes bibliográficos
Autor(a) principal: Martins, George Deroco [UNESP]
Data de Publicação: 2017
Outros Autores: Bueno Trindade Galo, Maria de Lourdes [UNESP], Vieira, Bruno Sergio
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/JSTARS.2017.2737618
http://hdl.handle.net/11449/163672
Resumo: Nematodes are a serious issue for coffee cultivation in Brazil. Because root infection by nematodes induces spectral variation in leaves and defines a unique spatial configuration in the cultivation field, the aim of this study was to use biophysical parameters and remote sensing data to discriminate and map healthy, moderately infected, and severely infected coffee plants. An experimental area in southern Minas Gerais State, in which the occurrence of nematodes was certified, was selected, and biophysical and spectral measurements of the leaves were made. Hyperspectral data were also used in a band simulation of the RapidEye sensor to identify the most sensitive spectral ranges for pathogen discrimination in coffee plants. These bands, plus a normalized difference vegetation index image, were used for a multispectral classification of healthy and nematode-infected areas. None of the biophysical parameters efficiently discriminated the leaves of healthy and infected plants, but the band simulation indicated that red, red edge, and near infrared spectral ranges were complementary to the discrimination of healthy coffee plants and the two levels of infection. The multispectral classification defined the spatial distribution of healthy, moderately infected, and severely infected coffee plants, with an overall accuracy of 78% and Kappa coefficient of 0.71. Consideringthe degree of uncertainty and high cost involved in conventional detection of soil parasites, thelevels of accuracy achieved were adequate.
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spelling Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing MeasurementsCoffee plantationdisease detectionmappingnematodesprecision agricultureremote sensingspectral characterizationNematodes are a serious issue for coffee cultivation in Brazil. Because root infection by nematodes induces spectral variation in leaves and defines a unique spatial configuration in the cultivation field, the aim of this study was to use biophysical parameters and remote sensing data to discriminate and map healthy, moderately infected, and severely infected coffee plants. An experimental area in southern Minas Gerais State, in which the occurrence of nematodes was certified, was selected, and biophysical and spectral measurements of the leaves were made. Hyperspectral data were also used in a band simulation of the RapidEye sensor to identify the most sensitive spectral ranges for pathogen discrimination in coffee plants. These bands, plus a normalized difference vegetation index image, were used for a multispectral classification of healthy and nematode-infected areas. None of the biophysical parameters efficiently discriminated the leaves of healthy and infected plants, but the band simulation indicated that red, red edge, and near infrared spectral ranges were complementary to the discrimination of healthy coffee plants and the two levels of infection. The multispectral classification defined the spatial distribution of healthy, moderately infected, and severely infected coffee plants, with an overall accuracy of 78% and Kappa coefficient of 0.71. Consideringthe degree of uncertainty and high cost involved in conventional detection of soil parasites, thelevels of accuracy achieved were adequate.Sao Paulo State Univ, Grad Program Cartog Sci, BR-19060900 Presidente Prudente, SP, BrazilSao Paulo State Univ, Dept Cartog, BR-19060900 Presidente Prudente, SP, BrazilUniv Fed Uberlandia, Inst Agr Sci, BR-38500000 Mt Carmelo, MG, BrazilSao Paulo State Univ, Grad Program Cartog Sci, BR-19060900 Presidente Prudente, SP, BrazilSao Paulo State Univ, Dept Cartog, BR-19060900 Presidente Prudente, SP, BrazilIeee-inst Electrical Electronics Engineers IncUniversidade Estadual Paulista (Unesp)Universidade Federal de Uberlândia (UFU)Martins, George Deroco [UNESP]Bueno Trindade Galo, Maria de Lourdes [UNESP]Vieira, Bruno Sergio2018-11-26T17:44:31Z2018-11-26T17:44:31Z2017-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article5395-5403application/pdfhttp://dx.doi.org/10.1109/JSTARS.2017.2737618Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 10, n. 12, p. 5395-5403, 2017.1939-1404http://hdl.handle.net/11449/16367210.1109/JSTARS.2017.2737618WOS:000418871200007WOS000418871200007.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIeee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing1,547info:eu-repo/semantics/openAccess2024-06-18T15:01:26Zoai:repositorio.unesp.br:11449/163672Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:04:11.569088Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements
title Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements
spellingShingle Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements
Martins, George Deroco [UNESP]
Coffee plantation
disease detection
mapping
nematodes
precision agriculture
remote sensing
spectral characterization
title_short Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements
title_full Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements
title_fullStr Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements
title_full_unstemmed Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements
title_sort Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements
author Martins, George Deroco [UNESP]
author_facet Martins, George Deroco [UNESP]
Bueno Trindade Galo, Maria de Lourdes [UNESP]
Vieira, Bruno Sergio
author_role author
author2 Bueno Trindade Galo, Maria de Lourdes [UNESP]
Vieira, Bruno Sergio
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade Federal de Uberlândia (UFU)
dc.contributor.author.fl_str_mv Martins, George Deroco [UNESP]
Bueno Trindade Galo, Maria de Lourdes [UNESP]
Vieira, Bruno Sergio
dc.subject.por.fl_str_mv Coffee plantation
disease detection
mapping
nematodes
precision agriculture
remote sensing
spectral characterization
topic Coffee plantation
disease detection
mapping
nematodes
precision agriculture
remote sensing
spectral characterization
description Nematodes are a serious issue for coffee cultivation in Brazil. Because root infection by nematodes induces spectral variation in leaves and defines a unique spatial configuration in the cultivation field, the aim of this study was to use biophysical parameters and remote sensing data to discriminate and map healthy, moderately infected, and severely infected coffee plants. An experimental area in southern Minas Gerais State, in which the occurrence of nematodes was certified, was selected, and biophysical and spectral measurements of the leaves were made. Hyperspectral data were also used in a band simulation of the RapidEye sensor to identify the most sensitive spectral ranges for pathogen discrimination in coffee plants. These bands, plus a normalized difference vegetation index image, were used for a multispectral classification of healthy and nematode-infected areas. None of the biophysical parameters efficiently discriminated the leaves of healthy and infected plants, but the band simulation indicated that red, red edge, and near infrared spectral ranges were complementary to the discrimination of healthy coffee plants and the two levels of infection. The multispectral classification defined the spatial distribution of healthy, moderately infected, and severely infected coffee plants, with an overall accuracy of 78% and Kappa coefficient of 0.71. Consideringthe degree of uncertainty and high cost involved in conventional detection of soil parasites, thelevels of accuracy achieved were adequate.
publishDate 2017
dc.date.none.fl_str_mv 2017-12-01
2018-11-26T17:44:31Z
2018-11-26T17:44:31Z
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 http://dx.doi.org/10.1109/JSTARS.2017.2737618
Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 10, n. 12, p. 5395-5403, 2017.
1939-1404
http://hdl.handle.net/11449/163672
10.1109/JSTARS.2017.2737618
WOS:000418871200007
WOS000418871200007.pdf
url http://dx.doi.org/10.1109/JSTARS.2017.2737618
http://hdl.handle.net/11449/163672
identifier_str_mv Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 10, n. 12, p. 5395-5403, 2017.
1939-1404
10.1109/JSTARS.2017.2737618
WOS:000418871200007
WOS000418871200007.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing
1,547
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 5395-5403
application/pdf
dc.publisher.none.fl_str_mv Ieee-inst Electrical Electronics Engineers Inc
publisher.none.fl_str_mv Ieee-inst Electrical Electronics Engineers Inc
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
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