DETERMINATION OF NUTRITIONAL DEFICIENCY LEVEL OF NITROGEN IN BEAN USING ARTIFICIAL NEURAL NETWORKS

Detalhes bibliográficos
Autor(a) principal: Baesso, Murilo Mesquita
Data de Publicação: 2012
Outros Autores: Varella, Carlos Alberto Alves, Martins, Guilherme Augusto, Modolo, Alcir José, Brandelero, Evandro Martin
Tipo de documento: Artigo
Idioma: por
Título da fonte: Engenharia na Agricultura
Texto Completo: https://periodicos.ufv.br/reveng/article/view/273
Resumo: The objective of this study was to identify nutritional deficiency of nitrogen in bean using spectral indices and techniques of digital image processing. For that it was developed artificial neural networks with different numbers of neurons. Following the acquisition of images by a digital camera, they were cut into blocks with size of 20x20 and 40x40 pixels. Artificial neural networks were able to identify the different levels of nitrogen applied in the plants. The results using images acquired with 30 and 40 days after emergence were not different.
id UFV-2_dc65f76b839d3d952f091466cc241def
oai_identifier_str oai:ojs.periodicos.ufv.br:article/273
network_acronym_str UFV-2
network_name_str Engenharia na Agricultura
repository_id_str
spelling DETERMINATION OF NUTRITIONAL DEFICIENCY LEVEL OF NITROGEN IN BEAN USING ARTIFICIAL NEURAL NETWORKSDETERMINAÇÃO DO NÍVEL DE DEFICIÊNCIA NUTRICIONAL DE NITROGÊNIO NO FEIJOEIRO UTILIZANDO REDES NEURAIS ARTIFICIAISagricultura de precisãoPhaseolus vulgarisprocessamento de imagens digitais e sensoriamento remotoThe objective of this study was to identify nutritional deficiency of nitrogen in bean using spectral indices and techniques of digital image processing. For that it was developed artificial neural networks with different numbers of neurons. Following the acquisition of images by a digital camera, they were cut into blocks with size of 20x20 and 40x40 pixels. Artificial neural networks were able to identify the different levels of nitrogen applied in the plants. The results using images acquired with 30 and 40 days after emergence were not different.O objetivo desse trabalho foi identificar a deficiência nutricional de nitrogênio no feijoeiro utilizando índices espectrais e técnicas de processamento de imagens digitais. Para isso, foram desenvolvidas redes neurais artificiais com diferentes números de neurônios. Após a aquisição das imagens por uma câmera digital, elas eram cortadas em blocos com tamanho de 20x20 e 40x40 pixels. As redes neurais artificiais conseguiram identificar as diferentes doses de nitrogênio aplicados nas plantas. Os resultados obtidos usando imagens adquiridas com 30 e 40 dias após a emergência não foram diferentes.Universidade Federal de Viçosa - UFV2012-12-27info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufv.br/reveng/article/view/27310.13083/reveng.v20i6.303Engineering in Agriculture; Vol. 20 No. 6 (2012); 512-518Revista Engenharia na Agricultura - REVENG; v. 20 n. 6 (2012); 512-5182175-68131414-398410.13083/reveng.v20i6reponame:Engenharia na Agriculturainstname:Universidade Federal de Viçosa (UFV)instacron:UFVporhttps://periodicos.ufv.br/reveng/article/view/273/204Baesso, Murilo MesquitaVarella, Carlos Alberto AlvesMartins, Guilherme AugustoModolo, Alcir JoséBrandelero, Evandro Martininfo:eu-repo/semantics/openAccess2023-01-19T13:06:51Zoai:ojs.periodicos.ufv.br:article/273Revistahttps://periodicos.ufv.br/revengPUBhttps://periodicos.ufv.br/reveng/oairevistaengenharianagricultura@gmail.com||andrerosa@ufv.br||tramitacao.reveng@gmail.com|| reveng@ufv.br2175-68131414-3984opendoar:2023-01-19T13:06:51Engenharia na Agricultura - Universidade Federal de Viçosa (UFV)false
dc.title.none.fl_str_mv DETERMINATION OF NUTRITIONAL DEFICIENCY LEVEL OF NITROGEN IN BEAN USING ARTIFICIAL NEURAL NETWORKS
DETERMINAÇÃO DO NÍVEL DE DEFICIÊNCIA NUTRICIONAL DE NITROGÊNIO NO FEIJOEIRO UTILIZANDO REDES NEURAIS ARTIFICIAIS
title DETERMINATION OF NUTRITIONAL DEFICIENCY LEVEL OF NITROGEN IN BEAN USING ARTIFICIAL NEURAL NETWORKS
spellingShingle DETERMINATION OF NUTRITIONAL DEFICIENCY LEVEL OF NITROGEN IN BEAN USING ARTIFICIAL NEURAL NETWORKS
Baesso, Murilo Mesquita
agricultura de precisão
Phaseolus vulgaris
processamento de imagens digitais e sensoriamento remoto
title_short DETERMINATION OF NUTRITIONAL DEFICIENCY LEVEL OF NITROGEN IN BEAN USING ARTIFICIAL NEURAL NETWORKS
title_full DETERMINATION OF NUTRITIONAL DEFICIENCY LEVEL OF NITROGEN IN BEAN USING ARTIFICIAL NEURAL NETWORKS
title_fullStr DETERMINATION OF NUTRITIONAL DEFICIENCY LEVEL OF NITROGEN IN BEAN USING ARTIFICIAL NEURAL NETWORKS
title_full_unstemmed DETERMINATION OF NUTRITIONAL DEFICIENCY LEVEL OF NITROGEN IN BEAN USING ARTIFICIAL NEURAL NETWORKS
title_sort DETERMINATION OF NUTRITIONAL DEFICIENCY LEVEL OF NITROGEN IN BEAN USING ARTIFICIAL NEURAL NETWORKS
author Baesso, Murilo Mesquita
author_facet Baesso, Murilo Mesquita
Varella, Carlos Alberto Alves
Martins, Guilherme Augusto
Modolo, Alcir José
Brandelero, Evandro Martin
author_role author
author2 Varella, Carlos Alberto Alves
Martins, Guilherme Augusto
Modolo, Alcir José
Brandelero, Evandro Martin
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Baesso, Murilo Mesquita
Varella, Carlos Alberto Alves
Martins, Guilherme Augusto
Modolo, Alcir José
Brandelero, Evandro Martin
dc.subject.por.fl_str_mv agricultura de precisão
Phaseolus vulgaris
processamento de imagens digitais e sensoriamento remoto
topic agricultura de precisão
Phaseolus vulgaris
processamento de imagens digitais e sensoriamento remoto
description The objective of this study was to identify nutritional deficiency of nitrogen in bean using spectral indices and techniques of digital image processing. For that it was developed artificial neural networks with different numbers of neurons. Following the acquisition of images by a digital camera, they were cut into blocks with size of 20x20 and 40x40 pixels. Artificial neural networks were able to identify the different levels of nitrogen applied in the plants. The results using images acquired with 30 and 40 days after emergence were not different.
publishDate 2012
dc.date.none.fl_str_mv 2012-12-27
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://periodicos.ufv.br/reveng/article/view/273
10.13083/reveng.v20i6.303
url https://periodicos.ufv.br/reveng/article/view/273
identifier_str_mv 10.13083/reveng.v20i6.303
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://periodicos.ufv.br/reveng/article/view/273/204
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Viçosa - UFV
publisher.none.fl_str_mv Universidade Federal de Viçosa - UFV
dc.source.none.fl_str_mv Engineering in Agriculture; Vol. 20 No. 6 (2012); 512-518
Revista Engenharia na Agricultura - REVENG; v. 20 n. 6 (2012); 512-518
2175-6813
1414-3984
10.13083/reveng.v20i6
reponame:Engenharia na Agricultura
instname:Universidade Federal de Viçosa (UFV)
instacron:UFV
instname_str Universidade Federal de Viçosa (UFV)
instacron_str UFV
institution UFV
reponame_str Engenharia na Agricultura
collection Engenharia na Agricultura
repository.name.fl_str_mv Engenharia na Agricultura - Universidade Federal de Viçosa (UFV)
repository.mail.fl_str_mv revistaengenharianagricultura@gmail.com||andrerosa@ufv.br||tramitacao.reveng@gmail.com|| reveng@ufv.br
_version_ 1800211144982396928

Registros relacionados