The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista ciência agronômica (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902022000100406 |
Resumo: | ABSTRACT This study is based on the principle that vegetation indexes (VIs), derived from the RGB color model obtained from digital images, can be used to characterize spectral signatures and classify Brachiaria brizantha cv. Xaraés according to nitrogen status (N). From colorimetric data obtained from leaf blade images acquired in the field, three artificial neural networks were evaluated according to the performance in the classification of N status: Feedforward Backpropagation (FFBP), Cascade Forward Backpropagation (CFBP) and Radial Base function (RBFNN). Four N fertilization rates were applied to generate contrasting N contents in the plants. The youngest completely expanded leaves from 60 tillers were detached at each regrowth cycle of 28 days, thus their images and leaf N content were obtained. Samples were then classified as deficient (>17 g N kg-1 leaf dry matter (DM), moderately deficient (from 17.1 to 20.0 g N kg-1 DM), and sufficient (> 20.1 g N kg-1 DM). The VIs were selected by principal component analysis and the performance of the networks evaluated by the accuracy. The accuracy in classification obtained by the networks were 88%, 86% and 79% for FFBP, CFBP and RBFNN, respectively, indicating that the spectral signatures can be determined from images acquired in the field. So, the proposed method could be used to develop a software that aims to monitor the status of N in real time, providing a fast and inexpensive tool for defining the time and the amount of N fertilizer, according to the pasture demand. |
id |
UFC-2_b0bd616adcfc2bb7025436e71611a192 |
---|---|
oai_identifier_str |
oai:scielo:S1806-66902022000100406 |
network_acronym_str |
UFC-2 |
network_name_str |
Revista ciência agronômica (Online) |
repository_id_str |
|
spelling |
The use of computer vision to classify Xaraés grass according to nutritional status in nitrogenImage processingRemote sensingHSBSpectral signatureABSTRACT This study is based on the principle that vegetation indexes (VIs), derived from the RGB color model obtained from digital images, can be used to characterize spectral signatures and classify Brachiaria brizantha cv. Xaraés according to nitrogen status (N). From colorimetric data obtained from leaf blade images acquired in the field, three artificial neural networks were evaluated according to the performance in the classification of N status: Feedforward Backpropagation (FFBP), Cascade Forward Backpropagation (CFBP) and Radial Base function (RBFNN). Four N fertilization rates were applied to generate contrasting N contents in the plants. The youngest completely expanded leaves from 60 tillers were detached at each regrowth cycle of 28 days, thus their images and leaf N content were obtained. Samples were then classified as deficient (>17 g N kg-1 leaf dry matter (DM), moderately deficient (from 17.1 to 20.0 g N kg-1 DM), and sufficient (> 20.1 g N kg-1 DM). The VIs were selected by principal component analysis and the performance of the networks evaluated by the accuracy. The accuracy in classification obtained by the networks were 88%, 86% and 79% for FFBP, CFBP and RBFNN, respectively, indicating that the spectral signatures can be determined from images acquired in the field. So, the proposed method could be used to develop a software that aims to monitor the status of N in real time, providing a fast and inexpensive tool for defining the time and the amount of N fertilizer, according to the pasture demand.Universidade Federal do Ceará2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902022000100406Revista Ciência Agronômica v.53 2022reponame:Revista ciência agronômica (Online)instname:Universidade Federal do Ceará (UFC)instacron:UFC10.5935/1806-6690.20220006info:eu-repo/semantics/openAccessMancin,Wellington RenatoPereira,Lilian Elgalise TechioCarvalho,Rachel Santos BuenoShi,YeyinSilupu,Wilson Manuel CastroTech,Adriano Rogério Brunoeng2021-11-19T00:00:00Zoai:scielo:S1806-66902022000100406Revistahttp://www.ccarevista.ufc.br/PUBhttps://old.scielo.br/oai/scielo-oai.php||alekdutra@ufc.br|| ccarev@ufc.br1806-66900045-6888opendoar:2021-11-19T00:00Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC)false |
dc.title.none.fl_str_mv |
The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen |
title |
The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen |
spellingShingle |
The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen Mancin,Wellington Renato Image processing Remote sensing HSB Spectral signature |
title_short |
The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen |
title_full |
The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen |
title_fullStr |
The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen |
title_full_unstemmed |
The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen |
title_sort |
The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen |
author |
Mancin,Wellington Renato |
author_facet |
Mancin,Wellington Renato Pereira,Lilian Elgalise Techio Carvalho,Rachel Santos Bueno Shi,Yeyin Silupu,Wilson Manuel Castro Tech,Adriano Rogério Bruno |
author_role |
author |
author2 |
Pereira,Lilian Elgalise Techio Carvalho,Rachel Santos Bueno Shi,Yeyin Silupu,Wilson Manuel Castro Tech,Adriano Rogério Bruno |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Mancin,Wellington Renato Pereira,Lilian Elgalise Techio Carvalho,Rachel Santos Bueno Shi,Yeyin Silupu,Wilson Manuel Castro Tech,Adriano Rogério Bruno |
dc.subject.por.fl_str_mv |
Image processing Remote sensing HSB Spectral signature |
topic |
Image processing Remote sensing HSB Spectral signature |
description |
ABSTRACT This study is based on the principle that vegetation indexes (VIs), derived from the RGB color model obtained from digital images, can be used to characterize spectral signatures and classify Brachiaria brizantha cv. Xaraés according to nitrogen status (N). From colorimetric data obtained from leaf blade images acquired in the field, three artificial neural networks were evaluated according to the performance in the classification of N status: Feedforward Backpropagation (FFBP), Cascade Forward Backpropagation (CFBP) and Radial Base function (RBFNN). Four N fertilization rates were applied to generate contrasting N contents in the plants. The youngest completely expanded leaves from 60 tillers were detached at each regrowth cycle of 28 days, thus their images and leaf N content were obtained. Samples were then classified as deficient (>17 g N kg-1 leaf dry matter (DM), moderately deficient (from 17.1 to 20.0 g N kg-1 DM), and sufficient (> 20.1 g N kg-1 DM). The VIs were selected by principal component analysis and the performance of the networks evaluated by the accuracy. The accuracy in classification obtained by the networks were 88%, 86% and 79% for FFBP, CFBP and RBFNN, respectively, indicating that the spectral signatures can be determined from images acquired in the field. So, the proposed method could be used to develop a software that aims to monitor the status of N in real time, providing a fast and inexpensive tool for defining the time and the amount of N fertilizer, according to the pasture demand. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
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=S1806-66902022000100406 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902022000100406 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.5935/1806-6690.20220006 |
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 |
Universidade Federal do Ceará |
publisher.none.fl_str_mv |
Universidade Federal do Ceará |
dc.source.none.fl_str_mv |
Revista Ciência Agronômica v.53 2022 reponame:Revista ciência agronômica (Online) instname:Universidade Federal do Ceará (UFC) instacron:UFC |
instname_str |
Universidade Federal do Ceará (UFC) |
instacron_str |
UFC |
institution |
UFC |
reponame_str |
Revista ciência agronômica (Online) |
collection |
Revista ciência agronômica (Online) |
repository.name.fl_str_mv |
Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC) |
repository.mail.fl_str_mv |
||alekdutra@ufc.br|| ccarev@ufc.br |
_version_ |
1750297490353029120 |