Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks

Detalhes bibliográficos
Autor(a) principal: Covolan Ulson, J. A. [UNESP]
Data de Publicação: 2001
Outros Autores: Benez, S. H. [UNESP], Nunes Da Silva, I. [UNESP], Nunes De Souza, A. [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/224199
Resumo: The accurate identification of the nitrogen content in crop plants is extremely important since it involves economic aspects and environmental impacts. Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification involves a lot of nonlinear parameters and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought spectral reflectance of plant leaves using artificial neural networks. The network acts as identifier of relationships among pH of soil, fertilizer treatment, spectral reflectance and nitrogen content in the plants. So, nitrogen content can be estimated and generalized from an input parameter set. This approach can be form the basis for development of an accurate real time nitrogen applicator.
id UNSP_36801495c3c1d2e3cf90020f26e7c300
oai_identifier_str oai:repositorio.unesp.br:11449/224199
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling Nitrogen content identification in crop plants using spectral reflectance and artificial neural networksThe accurate identification of the nitrogen content in crop plants is extremely important since it involves economic aspects and environmental impacts. Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification involves a lot of nonlinear parameters and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought spectral reflectance of plant leaves using artificial neural networks. The network acts as identifier of relationships among pH of soil, fertilizer treatment, spectral reflectance and nitrogen content in the plants. So, nitrogen content can be estimated and generalized from an input parameter set. This approach can be form the basis for development of an accurate real time nitrogen applicator.University of São Paulo-UNESP Department of Electrical Engineering, CP 473, CEP 17033-360, Bauru-SPUniversity of São Paulo-UNESP Department of Electrical Engineering, CP 473, CEP 17033-360, Bauru-SPUniversidade Estadual Paulista (UNESP)Covolan Ulson, J. A. [UNESP]Benez, S. H. [UNESP]Nunes Da Silva, I. [UNESP]Nunes De Souza, A. [UNESP]2022-04-28T19:55:06Z2022-04-28T19:55:06Z2001-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject2088-2092Proceedings of the International Joint Conference on Neural Networks, v. 3, p. 2088-2092.http://hdl.handle.net/11449/2241992-s2.0-0034868966Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the International Joint Conference on Neural Networksinfo:eu-repo/semantics/openAccess2022-04-28T19:55:07Zoai:repositorio.unesp.br:11449/224199Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T19:55:07Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks
title Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks
spellingShingle Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks
Covolan Ulson, J. A. [UNESP]
title_short Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks
title_full Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks
title_fullStr Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks
title_full_unstemmed Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks
title_sort Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks
author Covolan Ulson, J. A. [UNESP]
author_facet Covolan Ulson, J. A. [UNESP]
Benez, S. H. [UNESP]
Nunes Da Silva, I. [UNESP]
Nunes De Souza, A. [UNESP]
author_role author
author2 Benez, S. H. [UNESP]
Nunes Da Silva, I. [UNESP]
Nunes De Souza, A. [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Covolan Ulson, J. A. [UNESP]
Benez, S. H. [UNESP]
Nunes Da Silva, I. [UNESP]
Nunes De Souza, A. [UNESP]
description The accurate identification of the nitrogen content in crop plants is extremely important since it involves economic aspects and environmental impacts. Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification involves a lot of nonlinear parameters and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought spectral reflectance of plant leaves using artificial neural networks. The network acts as identifier of relationships among pH of soil, fertilizer treatment, spectral reflectance and nitrogen content in the plants. So, nitrogen content can be estimated and generalized from an input parameter set. This approach can be form the basis for development of an accurate real time nitrogen applicator.
publishDate 2001
dc.date.none.fl_str_mv 2001-01-01
2022-04-28T19:55:06Z
2022-04-28T19:55:06Z
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 Proceedings of the International Joint Conference on Neural Networks, v. 3, p. 2088-2092.
http://hdl.handle.net/11449/224199
2-s2.0-0034868966
identifier_str_mv Proceedings of the International Joint Conference on Neural Networks, v. 3, p. 2088-2092.
2-s2.0-0034868966
url http://hdl.handle.net/11449/224199
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings of the International Joint Conference on Neural Networks
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 2088-2092
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_ 1803046019511877632