Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops

Descripción del Articulo

Rayner would like to thank Cienciactiva, an initiative of the National Council of Science, Technology and Technological Innovation-CONCYTEC (Peru) for the financial research support and scholarship.
Detalles Bibliográficos
Autores: Condori, RHM, Romualdo, LM, Bruno, OM, Luz, PHD
Formato: objeto de conferencia
Fecha de Publicación:2017
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/960
Enlace del recurso:https://hdl.handle.net/20.500.12390/960
https://doi.org/10.1109/WVC.2017.00009
Nivel de acceso:acceso abierto
Materia:transfer learning
Nutritional Assessment
maize leaf analysis
deep learning
texture analysis
convolutional neural networks
https://purl.org/pe-repo/ocde/ford#3.02.00
id CONC_de1877db210e576dd481e18d4e38531e
oai_identifier_str oai:repositorio.concytec.gob.pe:20.500.12390/960
network_acronym_str CONC
network_name_str CONCYTEC-Institucional
repository_id_str 4689
dc.title.none.fl_str_mv Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops
title Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops
spellingShingle Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops
Condori, RHM
transfer learning
Nutritional Assessment
maize leaf analysis
deep learning
texture analysis
convolutional neural networks
https://purl.org/pe-repo/ocde/ford#3.02.00
title_short Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops
title_full Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops
title_fullStr Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops
title_full_unstemmed Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops
title_sort Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops
author Condori, RHM
author_facet Condori, RHM
Romualdo, LM
Bruno, OM
Luz, PHD
author_role author
author2 Romualdo, LM
Bruno, OM
Luz, PHD
author2_role author
author
author
dc.contributor.author.fl_str_mv Condori, RHM
Romualdo, LM
Bruno, OM
Luz, PHD
dc.subject.none.fl_str_mv transfer learning
topic transfer learning
Nutritional Assessment
maize leaf analysis
deep learning
texture analysis
convolutional neural networks
https://purl.org/pe-repo/ocde/ford#3.02.00
dc.subject.es_PE.fl_str_mv Nutritional Assessment
maize leaf analysis
deep learning
texture analysis
convolutional neural networks
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#3.02.00
description Rayner would like to thank Cienciactiva, an initiative of the National Council of Science, Technology and Technological Innovation-CONCYTEC (Peru) for the financial research support and scholarship.
publishDate 2017
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.isbn.none.fl_str_mv 978-1-5386-1451-8
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/960
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1109/WVC.2017.00009
dc.identifier.isi.none.fl_str_mv 463846300014
identifier_str_mv 978-1-5386-1451-8
463846300014
url https://hdl.handle.net/20.500.12390/960
https://doi.org/10.1109/WVC.2017.00009
dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv IEEE Computer Society
publisher.none.fl_str_mv IEEE Computer Society
dc.source.none.fl_str_mv reponame:CONCYTEC-Institucional
instname:Consejo Nacional de Ciencia Tecnología e Innovación
instacron:CONCYTEC
instname_str Consejo Nacional de Ciencia Tecnología e Innovación
instacron_str CONCYTEC
institution CONCYTEC
reponame_str CONCYTEC-Institucional
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
_version_ 1844883122661883904
spelling Publicationrp00841500rp02621600rp00842500rp02622600Condori, RHMRomualdo, LMBruno, OMLuz, PHD2024-05-30T23:13:38Z2024-05-30T23:13:38Z2017978-1-5386-1451-8https://hdl.handle.net/20.500.12390/960https://doi.org/10.1109/WVC.2017.00009463846300014Rayner would like to thank Cienciactiva, an initiative of the National Council of Science, Technology and Technological Innovation-CONCYTEC (Peru) for the financial research support and scholarship.Every year, efficient maize production is very important to the economy of many countries. Since nutritional deficiencies in maize plants are directly reflected in their grains productivity, early detection is needed to maximize the chances of proper recovery of these plants. Traditional texture methods recently showed interesting results in the identification of nutritional deficiencies. On the other hand, deep learning techniques are increasingly outperforming hand-crafted features on many tasks. In this paper, we propose a simple transfer learning approach from pre-trained cnn models and compare their results with those from traditional texture methods in the task of nitrogen deficiency identification. We perform experiments in a real-world dataset that contains digitalized images of maize leaves at different growth stages and with different levels of nitrogen fertilization. The results show that deep learning based descriptors achieve better success rates than traditional texture methods.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengIEEE Computer Societyinfo:eu-repo/semantics/openAccesstransfer learningNutritional Assessment-1maize leaf analysis-1deep learning-1texture analysis-1convolutional neural networks-1https://purl.org/pe-repo/ocde/ford#3.02.00-1Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize cropsinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/960oai:repositorio.concytec.gob.pe:20.500.12390/9602024-05-30 15:23:20.891http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="6f4b1bbb-257b-438c-b069-50ab2f3763f3"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <DOI>https://doi.org/10.1109/WVC.2017.00009</DOI> <ISI-Number>463846300014</ISI-Number> <ISBN>978-1-5386-1451-8</ISBN> <Authors> <Author> <DisplayName>Condori, RHM</DisplayName> <Person id="rp00841" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Romualdo, LM</DisplayName> <Person id="rp02621" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Bruno, OM</DisplayName> <Person id="rp00842" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Luz, PHD</DisplayName> <Person id="rp02622" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>IEEE Computer Society</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>transfer learning</Keyword> <Keyword>Nutritional Assessment</Keyword> <Keyword>maize leaf analysis</Keyword> <Keyword>deep learning</Keyword> <Keyword>texture analysis</Keyword> <Keyword>convolutional neural networks</Keyword> <Abstract>Every year, efficient maize production is very important to the economy of many countries. Since nutritional deficiencies in maize plants are directly reflected in their grains productivity, early detection is needed to maximize the chances of proper recovery of these plants. Traditional texture methods recently showed interesting results in the identification of nutritional deficiencies. On the other hand, deep learning techniques are increasingly outperforming hand-crafted features on many tasks. In this paper, we propose a simple transfer learning approach from pre-trained cnn models and compare their results with those from traditional texture methods in the task of nitrogen deficiency identification. We perform experiments in a real-world dataset that contains digitalized images of maize leaves at different growth stages and with different levels of nitrogen fertilization. The results show that deep learning based descriptors achieve better success rates than traditional texture methods.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
score 13.425424
Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).

Ejemplares Similares