USE OF DIGITAL IMAGES TO CLASSIFY LEAF PHOSPHORUS CONTENT IN GRAPE TOMATOES
Autor(a) principal: | |
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Data de Publicação: | 2022 |
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=S0100-69162022000800102 |
Resumo: | ABSTRACT Leaf chemical analysis is one of the ways to assess plant development. However, this type of assessment is expensive and time-consuming. The variation of nutrient content in the leaves modifies the proportion of light reflected and absorbed by plants at different wavelengths. Being able to relate the color reflected by the leaves with their phosphorus (P) content and using this data as input into an artificial neural network (ANN) can be an alternative for its determination. For this, it is necessary to establish which colors are most correlated with the different nutrients. Therefore, the phosphorus content in tomato leaves was evaluated in this study, according to four treatments (0.25, 50, 75, and 100% of the P doses). Different vegetation indices were also evaluated using images of mini-tomato leaves through a principal component analysis to determine which ones would be suitable to serve as an input to an ANN (multilayer perceptron). DGCI (Dark Green Color Index) and Bn (Normalized Blue) were the indices most related to P content. The neural network obtained 90% accuracy in the classification after training using both sides of the leaves. |
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Engenharia Agrícola |
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USE OF DIGITAL IMAGES TO CLASSIFY LEAF PHOSPHORUS CONTENT IN GRAPE TOMATOESArtificial Neural Networkvegetation indicesDGCIMPRISolanum lycopersicumABSTRACT Leaf chemical analysis is one of the ways to assess plant development. However, this type of assessment is expensive and time-consuming. The variation of nutrient content in the leaves modifies the proportion of light reflected and absorbed by plants at different wavelengths. Being able to relate the color reflected by the leaves with their phosphorus (P) content and using this data as input into an artificial neural network (ANN) can be an alternative for its determination. For this, it is necessary to establish which colors are most correlated with the different nutrients. Therefore, the phosphorus content in tomato leaves was evaluated in this study, according to four treatments (0.25, 50, 75, and 100% of the P doses). Different vegetation indices were also evaluated using images of mini-tomato leaves through a principal component analysis to determine which ones would be suitable to serve as an input to an ANN (multilayer perceptron). DGCI (Dark Green Color Index) and Bn (Normalized Blue) were the indices most related to P content. The neural network obtained 90% accuracy in the classification after training using both sides of the leaves.Associação Brasileira de Engenharia Agrícola2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162022000800102Engenharia Agrícola v.42 n.spe 2022reponame:Engenharia Agrícolainstname:Associação Brasileira de Engenharia Agrícola (SBEA)instacron:SBEA10.1590/1809-4430-eng.agric.v42nepe20210147/2022info:eu-repo/semantics/openAccessMagalhães,Leonardo P. deTrevisan,Lucas R.Gomes,Tamara M.Rossi,Fabrícioeng2022-03-24T00:00:00Zoai:scielo:S0100-69162022000800102Revistahttp://www.engenhariaagricola.org.br/ORGhttps://old.scielo.br/oai/scielo-oai.phprevistasbea@sbea.org.br||sbea@sbea.org.br1809-44300100-6916opendoar:2022-03-24T00:00Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)false |
dc.title.none.fl_str_mv |
USE OF DIGITAL IMAGES TO CLASSIFY LEAF PHOSPHORUS CONTENT IN GRAPE TOMATOES |
title |
USE OF DIGITAL IMAGES TO CLASSIFY LEAF PHOSPHORUS CONTENT IN GRAPE TOMATOES |
spellingShingle |
USE OF DIGITAL IMAGES TO CLASSIFY LEAF PHOSPHORUS CONTENT IN GRAPE TOMATOES Magalhães,Leonardo P. de Artificial Neural Network vegetation indices DGCI MPRI Solanum lycopersicum |
title_short |
USE OF DIGITAL IMAGES TO CLASSIFY LEAF PHOSPHORUS CONTENT IN GRAPE TOMATOES |
title_full |
USE OF DIGITAL IMAGES TO CLASSIFY LEAF PHOSPHORUS CONTENT IN GRAPE TOMATOES |
title_fullStr |
USE OF DIGITAL IMAGES TO CLASSIFY LEAF PHOSPHORUS CONTENT IN GRAPE TOMATOES |
title_full_unstemmed |
USE OF DIGITAL IMAGES TO CLASSIFY LEAF PHOSPHORUS CONTENT IN GRAPE TOMATOES |
title_sort |
USE OF DIGITAL IMAGES TO CLASSIFY LEAF PHOSPHORUS CONTENT IN GRAPE TOMATOES |
author |
Magalhães,Leonardo P. de |
author_facet |
Magalhães,Leonardo P. de Trevisan,Lucas R. Gomes,Tamara M. Rossi,Fabrício |
author_role |
author |
author2 |
Trevisan,Lucas R. Gomes,Tamara M. Rossi,Fabrício |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Magalhães,Leonardo P. de Trevisan,Lucas R. Gomes,Tamara M. Rossi,Fabrício |
dc.subject.por.fl_str_mv |
Artificial Neural Network vegetation indices DGCI MPRI Solanum lycopersicum |
topic |
Artificial Neural Network vegetation indices DGCI MPRI Solanum lycopersicum |
description |
ABSTRACT Leaf chemical analysis is one of the ways to assess plant development. However, this type of assessment is expensive and time-consuming. The variation of nutrient content in the leaves modifies the proportion of light reflected and absorbed by plants at different wavelengths. Being able to relate the color reflected by the leaves with their phosphorus (P) content and using this data as input into an artificial neural network (ANN) can be an alternative for its determination. For this, it is necessary to establish which colors are most correlated with the different nutrients. Therefore, the phosphorus content in tomato leaves was evaluated in this study, according to four treatments (0.25, 50, 75, and 100% of the P doses). Different vegetation indices were also evaluated using images of mini-tomato leaves through a principal component analysis to determine which ones would be suitable to serve as an input to an ANN (multilayer perceptron). DGCI (Dark Green Color Index) and Bn (Normalized Blue) were the indices most related to P content. The neural network obtained 90% accuracy in the classification after training using both sides of the leaves. |
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=S0100-69162022000800102 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162022000800102 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1809-4430-eng.agric.v42nepe20210147/2022 |
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.42 n.spe 2022 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_ |
1752126275469705216 |