CORRELATIONS AMONG VEGETATION INDICES AND PEANUT TRAITS DURING DIFFERENT CROP DEVELOPMENT STAGES
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Engenharia Agrícola |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000800033 |
Resumo: | ABSTRACT Active optical sensors have been widely used for the spatial and temporal monitoring of peanut culture because they are accurate, non-destructive methods for rapidly obtaining data. The objective of this study was to determine the optimal stage of crop growth for collecting sensor readings based on correlations between quality indicators. In addition, we compared vegetation indices (Normalized Difference Vegetation Index [NDVI], Normalized Difference Red-Edge Index, [NDRE], and Inverse Ratio Vegetation Index, [IRVI]) by monitoring temporal variability in the peanut crop in order to determine which of them obtained the best reading quality throughout the process. The experiment was performed on the 2016/17 crop in the agricultural area of the municipality of Dumont in the state of São Paulo, Brazil. The experimental design was based on the basic assumptions of statistical quality control and contained 63 sample points in a 30 × 30 m grid. The parameters were evaluated at 30, 45, 60, 75, and 119 days after sowing (DAS) using proximal sensing with GreenSeeker and OptRX sensors. We found that 45 and 60 DAS were the optimal times for monitoring peanut crop variability. For spatiotemporal monitoring of the culture with control charts, NDRE showed the best readings throughout the process when compared to NDVI and IRVI. |
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CORRELATIONS AMONG VEGETATION INDICES AND PEANUT TRAITS DURING DIFFERENT CROP DEVELOPMENT STAGESremote sensingArachis hypogaea L.control chartsprecision agricultureABSTRACT Active optical sensors have been widely used for the spatial and temporal monitoring of peanut culture because they are accurate, non-destructive methods for rapidly obtaining data. The objective of this study was to determine the optimal stage of crop growth for collecting sensor readings based on correlations between quality indicators. In addition, we compared vegetation indices (Normalized Difference Vegetation Index [NDVI], Normalized Difference Red-Edge Index, [NDRE], and Inverse Ratio Vegetation Index, [IRVI]) by monitoring temporal variability in the peanut crop in order to determine which of them obtained the best reading quality throughout the process. The experiment was performed on the 2016/17 crop in the agricultural area of the municipality of Dumont in the state of São Paulo, Brazil. The experimental design was based on the basic assumptions of statistical quality control and contained 63 sample points in a 30 × 30 m grid. The parameters were evaluated at 30, 45, 60, 75, and 119 days after sowing (DAS) using proximal sensing with GreenSeeker and OptRX sensors. We found that 45 and 60 DAS were the optimal times for monitoring peanut crop variability. For spatiotemporal monitoring of the culture with control charts, NDRE showed the best readings throughout the process when compared to NDVI and IRVI.Associação Brasileira de Engenharia Agrícola2019-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000800033Engenharia Agrícola v.39 n.spe 2019reponame:Engenharia Agrícolainstname:Associação Brasileira de Engenharia Agrícola (SBEA)instacron:SBEA10.1590/1809-4430-eng.agric.v39nep33-40/2019info:eu-repo/semantics/openAccessCarneiro,Franciele M.Furlani,Carlos E. A.Zerbato,CristianoMenezes,Patricia C. deGírio,Lucas A. da S.eng2019-09-05T00:00:00Zoai:scielo:S0100-69162019000800033Revistahttp://www.engenhariaagricola.org.br/ORGhttps://old.scielo.br/oai/scielo-oai.phprevistasbea@sbea.org.br||sbea@sbea.org.br1809-44300100-6916opendoar:2019-09-05T00:00Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)false |
dc.title.none.fl_str_mv |
CORRELATIONS AMONG VEGETATION INDICES AND PEANUT TRAITS DURING DIFFERENT CROP DEVELOPMENT STAGES |
title |
CORRELATIONS AMONG VEGETATION INDICES AND PEANUT TRAITS DURING DIFFERENT CROP DEVELOPMENT STAGES |
spellingShingle |
CORRELATIONS AMONG VEGETATION INDICES AND PEANUT TRAITS DURING DIFFERENT CROP DEVELOPMENT STAGES Carneiro,Franciele M. remote sensing Arachis hypogaea L. control charts precision agriculture |
title_short |
CORRELATIONS AMONG VEGETATION INDICES AND PEANUT TRAITS DURING DIFFERENT CROP DEVELOPMENT STAGES |
title_full |
CORRELATIONS AMONG VEGETATION INDICES AND PEANUT TRAITS DURING DIFFERENT CROP DEVELOPMENT STAGES |
title_fullStr |
CORRELATIONS AMONG VEGETATION INDICES AND PEANUT TRAITS DURING DIFFERENT CROP DEVELOPMENT STAGES |
title_full_unstemmed |
CORRELATIONS AMONG VEGETATION INDICES AND PEANUT TRAITS DURING DIFFERENT CROP DEVELOPMENT STAGES |
title_sort |
CORRELATIONS AMONG VEGETATION INDICES AND PEANUT TRAITS DURING DIFFERENT CROP DEVELOPMENT STAGES |
author |
Carneiro,Franciele M. |
author_facet |
Carneiro,Franciele M. Furlani,Carlos E. A. Zerbato,Cristiano Menezes,Patricia C. de Gírio,Lucas A. da S. |
author_role |
author |
author2 |
Furlani,Carlos E. A. Zerbato,Cristiano Menezes,Patricia C. de Gírio,Lucas A. da S. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Carneiro,Franciele M. Furlani,Carlos E. A. Zerbato,Cristiano Menezes,Patricia C. de Gírio,Lucas A. da S. |
dc.subject.por.fl_str_mv |
remote sensing Arachis hypogaea L. control charts precision agriculture |
topic |
remote sensing Arachis hypogaea L. control charts precision agriculture |
description |
ABSTRACT Active optical sensors have been widely used for the spatial and temporal monitoring of peanut culture because they are accurate, non-destructive methods for rapidly obtaining data. The objective of this study was to determine the optimal stage of crop growth for collecting sensor readings based on correlations between quality indicators. In addition, we compared vegetation indices (Normalized Difference Vegetation Index [NDVI], Normalized Difference Red-Edge Index, [NDRE], and Inverse Ratio Vegetation Index, [IRVI]) by monitoring temporal variability in the peanut crop in order to determine which of them obtained the best reading quality throughout the process. The experiment was performed on the 2016/17 crop in the agricultural area of the municipality of Dumont in the state of São Paulo, Brazil. The experimental design was based on the basic assumptions of statistical quality control and contained 63 sample points in a 30 × 30 m grid. The parameters were evaluated at 30, 45, 60, 75, and 119 days after sowing (DAS) using proximal sensing with GreenSeeker and OptRX sensors. We found that 45 and 60 DAS were the optimal times for monitoring peanut crop variability. For spatiotemporal monitoring of the culture with control charts, NDRE showed the best readings throughout the process when compared to NDVI and IRVI. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000800033 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000800033 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1809-4430-eng.agric.v39nep33-40/2019 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
dc.source.none.fl_str_mv |
Engenharia Agrícola v.39 n.spe 2019 reponame:Engenharia Agrícola instname:Associação Brasileira de Engenharia Agrícola (SBEA) instacron:SBEA |
instname_str |
Associação Brasileira de Engenharia Agrícola (SBEA) |
instacron_str |
SBEA |
institution |
SBEA |
reponame_str |
Engenharia Agrícola |
collection |
Engenharia Agrícola |
repository.name.fl_str_mv |
Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA) |
repository.mail.fl_str_mv |
revistasbea@sbea.org.br||sbea@sbea.org.br |
_version_ |
1752126274482995200 |