Using remote sensing to map in-field variability of peanut maturity
Autor(a) principal: | |
---|---|
Data de Publicação: | 2019 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3920/978-90-8686-888-9_75 http://hdl.handle.net/11449/198036 |
Resumo: | A study was conducted to assess if vegetation indices (VIs) could be used as indicators of peanut maturity. A 24 ha block of a rainfed field with clearly visible soil and crop variability was used. An unmanned aerial vehicle (UAV) equipped with a multispectral camera captured spectral reflectance from the peanut canopy. The reflectance data were used to evaluate several VIs as potential indicators of peanut maturity. Pearson's correlation and linear regression were used to evaluate the response of the VIs as well as the sensitivity of individual reflectance bands to peanut maturity. The red edge band was the most sensitive. The most responsive VIs were the non-linear index (NLI) and the modified non-linear index (MNLI) when red edge reflectance was substituted for red reflectance. |
id |
UNSP_aaa31d6c6338c12bbe2c71f5c813b266 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/198036 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
Using remote sensing to map in-field variability of peanut maturityMultispectral imagesRed edgeReflectanceUAVVegetation indexA study was conducted to assess if vegetation indices (VIs) could be used as indicators of peanut maturity. A 24 ha block of a rainfed field with clearly visible soil and crop variability was used. An unmanned aerial vehicle (UAV) equipped with a multispectral camera captured spectral reflectance from the peanut canopy. The reflectance data were used to evaluate several VIs as potential indicators of peanut maturity. Pearson's correlation and linear regression were used to evaluate the response of the VIs as well as the sensitivity of individual reflectance bands to peanut maturity. The red edge band was the most sensitive. The most responsive VIs were the non-linear index (NLI) and the modified non-linear index (MNLI) when red edge reflectance was substituted for red reflectance.University of Wisconsin - SuperiorUniversity of Georgia Tifton CampusSão Paulo State University (UNESP) Jaboticabal CampusSão Paulo State University (UNESP) Jaboticabal CampusTifton CampusUniversidade Estadual Paulista (Unesp)Santos, A. F. [UNESP]Lacerda, L. N.Gobbo, S.Tofannin, A.Silva, R. P. [UNESP]Vellidis, G.2020-12-12T00:57:13Z2020-12-12T00:57:13Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject605-611http://dx.doi.org/10.3920/978-90-8686-888-9_75Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019, p. 605-611.http://hdl.handle.net/11449/19803610.3920/978-90-8686-888-9_752-s2.0-85073749480Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPrecision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019info:eu-repo/semantics/openAccess2021-10-23T07:59:21Zoai:repositorio.unesp.br:11449/198036Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:52:09.391784Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Using remote sensing to map in-field variability of peanut maturity |
title |
Using remote sensing to map in-field variability of peanut maturity |
spellingShingle |
Using remote sensing to map in-field variability of peanut maturity Santos, A. F. [UNESP] Multispectral images Red edge Reflectance UAV Vegetation index |
title_short |
Using remote sensing to map in-field variability of peanut maturity |
title_full |
Using remote sensing to map in-field variability of peanut maturity |
title_fullStr |
Using remote sensing to map in-field variability of peanut maturity |
title_full_unstemmed |
Using remote sensing to map in-field variability of peanut maturity |
title_sort |
Using remote sensing to map in-field variability of peanut maturity |
author |
Santos, A. F. [UNESP] |
author_facet |
Santos, A. F. [UNESP] Lacerda, L. N. Gobbo, S. Tofannin, A. Silva, R. P. [UNESP] Vellidis, G. |
author_role |
author |
author2 |
Lacerda, L. N. Gobbo, S. Tofannin, A. Silva, R. P. [UNESP] Vellidis, G. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Tifton Campus Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Santos, A. F. [UNESP] Lacerda, L. N. Gobbo, S. Tofannin, A. Silva, R. P. [UNESP] Vellidis, G. |
dc.subject.por.fl_str_mv |
Multispectral images Red edge Reflectance UAV Vegetation index |
topic |
Multispectral images Red edge Reflectance UAV Vegetation index |
description |
A study was conducted to assess if vegetation indices (VIs) could be used as indicators of peanut maturity. A 24 ha block of a rainfed field with clearly visible soil and crop variability was used. An unmanned aerial vehicle (UAV) equipped with a multispectral camera captured spectral reflectance from the peanut canopy. The reflectance data were used to evaluate several VIs as potential indicators of peanut maturity. Pearson's correlation and linear regression were used to evaluate the response of the VIs as well as the sensitivity of individual reflectance bands to peanut maturity. The red edge band was the most sensitive. The most responsive VIs were the non-linear index (NLI) and the modified non-linear index (MNLI) when red edge reflectance was substituted for red reflectance. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 2020-12-12T00:57:13Z 2020-12-12T00:57:13Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.3920/978-90-8686-888-9_75 Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019, p. 605-611. http://hdl.handle.net/11449/198036 10.3920/978-90-8686-888-9_75 2-s2.0-85073749480 |
url |
http://dx.doi.org/10.3920/978-90-8686-888-9_75 http://hdl.handle.net/11449/198036 |
identifier_str_mv |
Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019, p. 605-611. 10.3920/978-90-8686-888-9_75 2-s2.0-85073749480 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
605-611 |
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_ |
1808129132110807040 |