High-resolution satellite image to predict peanut maturity variability in commercial fields

Detalhes bibliográficos
Autor(a) principal: dos Santos, Adão Felipe
Data de Publicação: 2021
Outros Autores: Corrêa, Lígia Negri [UNESP], Lacerda, Lorena Nunes, Tedesco-Oliveira, Danilo [UNESP], Pilon, Cristiane, Vellidis, George, da Silva, Rouverson Pereira [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/s11119-021-09791-1
http://hdl.handle.net/11449/208518
Resumo: One of the main problems in the peanut production process is to identify the pod maturity stage. Peanut plants have indeterminate growth, which leads to a high pod maturity variability within the same plant. Moreover, the actual method of determining maturity is destructive and highly subjectivity, which does not represent the overall variability in the field. Hence, the main goal of this study was to verify the possibility to estimate peanut maturity and its in-field variability using an alternative non-destructive method based on orbital remote sensing. High-resolution satellite images (~ 3 m) were obtained from the PlanetScope platform for two commercial peanut fields in São Paulo state, Brazil, during the reproductive stage of the peanut crop (89 to 118 days after sowing—DAS). The fields were divided into 54 plots (30 × 30 m). The maturity was obtained using the Hull Scrape method. All Vegetation Indices (VIs) used showed a high Pearson correlation (p < 0.001) between peanut maturity and the VIs, with values decreasing as maturity increased. Non-Linear Index (NLI) values from 0.561 to 0.465 suggested that pods reached greater maturity than 74% (inflection point). The results found in this study indicated a great potential to use high-resolution satellite images to predict peanut maturity variability in commercial field. In addition, the proposed method contributes to monitoring the dynamics spatio-temporal of maturity progression, allowing for more accurate in-season and inversion management strategies in peanut.
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spelling High-resolution satellite image to predict peanut maturity variability in commercial fieldsArachis hypogaea LPlanetScope imagesPrecision harvestRemote sensingVegetation indicesOne of the main problems in the peanut production process is to identify the pod maturity stage. Peanut plants have indeterminate growth, which leads to a high pod maturity variability within the same plant. Moreover, the actual method of determining maturity is destructive and highly subjectivity, which does not represent the overall variability in the field. Hence, the main goal of this study was to verify the possibility to estimate peanut maturity and its in-field variability using an alternative non-destructive method based on orbital remote sensing. High-resolution satellite images (~ 3 m) were obtained from the PlanetScope platform for two commercial peanut fields in São Paulo state, Brazil, during the reproductive stage of the peanut crop (89 to 118 days after sowing—DAS). The fields were divided into 54 plots (30 × 30 m). The maturity was obtained using the Hull Scrape method. All Vegetation Indices (VIs) used showed a high Pearson correlation (p < 0.001) between peanut maturity and the VIs, with values decreasing as maturity increased. Non-Linear Index (NLI) values from 0.561 to 0.465 suggested that pods reached greater maturity than 74% (inflection point). The results found in this study indicated a great potential to use high-resolution satellite images to predict peanut maturity variability in commercial field. In addition, the proposed method contributes to monitoring the dynamics spatio-temporal of maturity progression, allowing for more accurate in-season and inversion management strategies in peanut.Department of Agriculture Lavras Federal University (UFLA), Aquenta SolDepartment of Engineering and Exact Sciences São Paulo State University (UNESP), Via Access Prof. Paulo Donato Castellane s/nDepartment of Crop and Soil Sciences University of Georgia, Tifton Campus, 2360 Rainwater RoadDepartment of Engineering and Exact Sciences São Paulo State University (UNESP), Via Access Prof. Paulo Donato Castellane s/nUniversidade Federal de Lavras (UFLA)Universidade Estadual Paulista (Unesp)University of Georgiados Santos, Adão FelipeCorrêa, Lígia Negri [UNESP]Lacerda, Lorena NunesTedesco-Oliveira, Danilo [UNESP]Pilon, CristianeVellidis, Georgeda Silva, Rouverson Pereira [UNESP]2021-06-25T11:13:27Z2021-06-25T11:13:27Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s11119-021-09791-1Precision Agriculture.1573-16181385-2256http://hdl.handle.net/11449/20851810.1007/s11119-021-09791-12-s2.0-85102937884Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPrecision Agricultureinfo:eu-repo/semantics/openAccess2024-06-06T15:18:42Zoai:repositorio.unesp.br:11449/208518Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:27:49.355650Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv High-resolution satellite image to predict peanut maturity variability in commercial fields
title High-resolution satellite image to predict peanut maturity variability in commercial fields
spellingShingle High-resolution satellite image to predict peanut maturity variability in commercial fields
dos Santos, Adão Felipe
Arachis hypogaea L
PlanetScope images
Precision harvest
Remote sensing
Vegetation indices
title_short High-resolution satellite image to predict peanut maturity variability in commercial fields
title_full High-resolution satellite image to predict peanut maturity variability in commercial fields
title_fullStr High-resolution satellite image to predict peanut maturity variability in commercial fields
title_full_unstemmed High-resolution satellite image to predict peanut maturity variability in commercial fields
title_sort High-resolution satellite image to predict peanut maturity variability in commercial fields
author dos Santos, Adão Felipe
author_facet dos Santos, Adão Felipe
Corrêa, Lígia Negri [UNESP]
Lacerda, Lorena Nunes
Tedesco-Oliveira, Danilo [UNESP]
Pilon, Cristiane
Vellidis, George
da Silva, Rouverson Pereira [UNESP]
author_role author
author2 Corrêa, Lígia Negri [UNESP]
Lacerda, Lorena Nunes
Tedesco-Oliveira, Danilo [UNESP]
Pilon, Cristiane
Vellidis, George
da Silva, Rouverson Pereira [UNESP]
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Federal de Lavras (UFLA)
Universidade Estadual Paulista (Unesp)
University of Georgia
dc.contributor.author.fl_str_mv dos Santos, Adão Felipe
Corrêa, Lígia Negri [UNESP]
Lacerda, Lorena Nunes
Tedesco-Oliveira, Danilo [UNESP]
Pilon, Cristiane
Vellidis, George
da Silva, Rouverson Pereira [UNESP]
dc.subject.por.fl_str_mv Arachis hypogaea L
PlanetScope images
Precision harvest
Remote sensing
Vegetation indices
topic Arachis hypogaea L
PlanetScope images
Precision harvest
Remote sensing
Vegetation indices
description One of the main problems in the peanut production process is to identify the pod maturity stage. Peanut plants have indeterminate growth, which leads to a high pod maturity variability within the same plant. Moreover, the actual method of determining maturity is destructive and highly subjectivity, which does not represent the overall variability in the field. Hence, the main goal of this study was to verify the possibility to estimate peanut maturity and its in-field variability using an alternative non-destructive method based on orbital remote sensing. High-resolution satellite images (~ 3 m) were obtained from the PlanetScope platform for two commercial peanut fields in São Paulo state, Brazil, during the reproductive stage of the peanut crop (89 to 118 days after sowing—DAS). The fields were divided into 54 plots (30 × 30 m). The maturity was obtained using the Hull Scrape method. All Vegetation Indices (VIs) used showed a high Pearson correlation (p < 0.001) between peanut maturity and the VIs, with values decreasing as maturity increased. Non-Linear Index (NLI) values from 0.561 to 0.465 suggested that pods reached greater maturity than 74% (inflection point). The results found in this study indicated a great potential to use high-resolution satellite images to predict peanut maturity variability in commercial field. In addition, the proposed method contributes to monitoring the dynamics spatio-temporal of maturity progression, allowing for more accurate in-season and inversion management strategies in peanut.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-25T11:13:27Z
2021-06-25T11:13:27Z
2021-01-01
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/10.1007/s11119-021-09791-1
Precision Agriculture.
1573-1618
1385-2256
http://hdl.handle.net/11449/208518
10.1007/s11119-021-09791-1
2-s2.0-85102937884
url http://dx.doi.org/10.1007/s11119-021-09791-1
http://hdl.handle.net/11449/208518
identifier_str_mv Precision Agriculture.
1573-1618
1385-2256
10.1007/s11119-021-09791-1
2-s2.0-85102937884
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Precision Agriculture
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
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)
repository.mail.fl_str_mv
_version_ 1808129322884530176