Image segmentation with artificial neural network for nutrient deficiency in cotton crop
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3844/jcssp.2014.1084.1093 http://hdl.handle.net/11449/171497 |
Resumo: | The leaf analysis in a crop can present the need of a nutrient determined in the plant. The macronutrients deficiency in the cotton crop can be identified by specific type of colors variation by leaves images. Early identification of macronutrients deficiency can help in the growing suitable of the crop and reduce the use of agricultural inputs. This study investigates the image segmentation of the cotton leaves with deficiency of the phosphor. The segmentation is performed by difference of leaf pigmentation, according with the pattern related to macronutrient type in deficit and the cultivate. The image segmentation is made by an artificial neural network and the Otsu method. The results show satisfactory values with an optimized artificial neural network and better than the Otsu method. The results are presented by images and distinct parameters of quality analysis in the segmentation. © 2014 Science Publications. |
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Image segmentation with artificial neural network for nutrient deficiency in cotton cropArtificial neural networkCottonImage segmentationOtsu methodPrecision agricultureThe leaf analysis in a crop can present the need of a nutrient determined in the plant. The macronutrients deficiency in the cotton crop can be identified by specific type of colors variation by leaves images. Early identification of macronutrients deficiency can help in the growing suitable of the crop and reduce the use of agricultural inputs. This study investigates the image segmentation of the cotton leaves with deficiency of the phosphor. The segmentation is performed by difference of leaf pigmentation, according with the pattern related to macronutrient type in deficit and the cultivate. The image segmentation is made by an artificial neural network and the Otsu method. The results show satisfactory values with an optimized artificial neural network and better than the Otsu method. The results are presented by images and distinct parameters of quality analysis in the segmentation. © 2014 Science Publications.Department of Computing UNEMAT, Colider, MTDepartment of Electrical Engineering UNESP, Ilha Solteira, SPResearch in Management and Fertilization of Production System Fundaçao MT, Rondonópolis, MTDepartment of Electrical Engineering UNESP, Ilha Solteira, SPUNEMATUniversidade Estadual Paulista (Unesp)Fundaçao MTSartin, Maicon A. [UNESP]Da Silva, Alexandre C.R. [UNESP]Kappes, Claudinei2018-12-11T16:55:35Z2018-12-11T16:55:35Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1084-1093application/pdfhttp://dx.doi.org/10.3844/jcssp.2014.1084.1093Journal of Computer Science, v. 10, n. 6, p. 1084-1093, 2014.1549-3636http://hdl.handle.net/11449/17149710.3844/jcssp.2014.1084.10932-s2.0-848946136712-s2.0-84894613671.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Computer Science0,147info:eu-repo/semantics/openAccess2024-01-13T06:32:23Zoai:repositorio.unesp.br:11449/171497Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-01-13T06:32:23Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Image segmentation with artificial neural network for nutrient deficiency in cotton crop |
title |
Image segmentation with artificial neural network for nutrient deficiency in cotton crop |
spellingShingle |
Image segmentation with artificial neural network for nutrient deficiency in cotton crop Sartin, Maicon A. [UNESP] Artificial neural network Cotton Image segmentation Otsu method Precision agriculture |
title_short |
Image segmentation with artificial neural network for nutrient deficiency in cotton crop |
title_full |
Image segmentation with artificial neural network for nutrient deficiency in cotton crop |
title_fullStr |
Image segmentation with artificial neural network for nutrient deficiency in cotton crop |
title_full_unstemmed |
Image segmentation with artificial neural network for nutrient deficiency in cotton crop |
title_sort |
Image segmentation with artificial neural network for nutrient deficiency in cotton crop |
author |
Sartin, Maicon A. [UNESP] |
author_facet |
Sartin, Maicon A. [UNESP] Da Silva, Alexandre C.R. [UNESP] Kappes, Claudinei |
author_role |
author |
author2 |
Da Silva, Alexandre C.R. [UNESP] Kappes, Claudinei |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
UNEMAT Universidade Estadual Paulista (Unesp) Fundaçao MT |
dc.contributor.author.fl_str_mv |
Sartin, Maicon A. [UNESP] Da Silva, Alexandre C.R. [UNESP] Kappes, Claudinei |
dc.subject.por.fl_str_mv |
Artificial neural network Cotton Image segmentation Otsu method Precision agriculture |
topic |
Artificial neural network Cotton Image segmentation Otsu method Precision agriculture |
description |
The leaf analysis in a crop can present the need of a nutrient determined in the plant. The macronutrients deficiency in the cotton crop can be identified by specific type of colors variation by leaves images. Early identification of macronutrients deficiency can help in the growing suitable of the crop and reduce the use of agricultural inputs. This study investigates the image segmentation of the cotton leaves with deficiency of the phosphor. The segmentation is performed by difference of leaf pigmentation, according with the pattern related to macronutrient type in deficit and the cultivate. The image segmentation is made by an artificial neural network and the Otsu method. The results show satisfactory values with an optimized artificial neural network and better than the Otsu method. The results are presented by images and distinct parameters of quality analysis in the segmentation. © 2014 Science Publications. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-01-01 2018-12-11T16:55:35Z 2018-12-11T16:55:35Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.3844/jcssp.2014.1084.1093 Journal of Computer Science, v. 10, n. 6, p. 1084-1093, 2014. 1549-3636 http://hdl.handle.net/11449/171497 10.3844/jcssp.2014.1084.1093 2-s2.0-84894613671 2-s2.0-84894613671.pdf |
url |
http://dx.doi.org/10.3844/jcssp.2014.1084.1093 http://hdl.handle.net/11449/171497 |
identifier_str_mv |
Journal of Computer Science, v. 10, n. 6, p. 1084-1093, 2014. 1549-3636 10.3844/jcssp.2014.1084.1093 2-s2.0-84894613671 2-s2.0-84894613671.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of Computer Science 0,147 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1084-1093 application/pdf |
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_ |
1803650245096112128 |