Wavelet Transform Applied to Coffee Entomology
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/SPSympo51155.2020.9593404 http://hdl.handle.net/11449/234042 |
Resumo: | In this work, the design and development of a computational algorithm to assist in the management of insect pests in coffee plantations are presented, particularly for detecting the presence of cicadas. Acoustic signals, previously captured, are submitted to the proposed system which reads the raw data, converts them to the wavelet domain and groups them together based on the Bark Scale. Then, Paraconsistent Characteristics Analysis, appearing as a technique recently presented in the scientific literature and which had not yet been used for this purpose, serves as a basis for selecting the best filter banks so that they can be later delivered to a Support Vector Machine (SVM), responsible for the final step of signal identification. The accuracy of 100% was achieved in most of the 3600 tests performed, proving the viability of the implemented strategy, which has become minimally complex due to the optimization provided by the paraconsistent methodology. Finally, a prototype in the scope of Internet of Things is described to serve as a possibility of implantation in the field. |
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Repositório Institucional da UNESP |
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Wavelet Transform Applied to Coffee EntomologyIn this work, the design and development of a computational algorithm to assist in the management of insect pests in coffee plantations are presented, particularly for detecting the presence of cicadas. Acoustic signals, previously captured, are submitted to the proposed system which reads the raw data, converts them to the wavelet domain and groups them together based on the Bark Scale. Then, Paraconsistent Characteristics Analysis, appearing as a technique recently presented in the scientific literature and which had not yet been used for this purpose, serves as a basis for selecting the best filter banks so that they can be later delivered to a Support Vector Machine (SVM), responsible for the final step of signal identification. The accuracy of 100% was achieved in most of the 3600 tests performed, proving the viability of the implemented strategy, which has become minimally complex due to the optimization provided by the paraconsistent methodology. Finally, a prototype in the scope of Internet of Things is described to serve as a possibility of implantation in the field.Escola de Engenharia de São Carlos Universidade de São Paulo São, Carlos, SPUniversidade Estadual Paulista, S. J. do Rio Preto SPInstituto Federal de São Paulo, Catanduva SPUniversidade Estadual Paulista, S. J. do Rio Preto SPUniversidade de São Paulo (USP)Universidade Estadual Paulista (UNESP)Instituto Federal de São PauloLemos Escola, Joao PauloDa Silva, Ivan NunesGuido, Rodrigo Capobianco [UNESP]Fonseca, Everthon Silva2022-05-01T12:40:50Z2022-05-01T12:40:50Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject58-64http://dx.doi.org/10.1109/SPSympo51155.2020.95934042021 Signal Processing Symposium, SPSympo 2021, p. 58-64.http://hdl.handle.net/11449/23404210.1109/SPSympo51155.2020.95934042-s2.0-85123353947Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2021 Signal Processing Symposium, SPSympo 2021info:eu-repo/semantics/openAccess2022-05-01T12:40:50Zoai:repositorio.unesp.br:11449/234042Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-06T00:03:55.025178Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Wavelet Transform Applied to Coffee Entomology |
title |
Wavelet Transform Applied to Coffee Entomology |
spellingShingle |
Wavelet Transform Applied to Coffee Entomology Lemos Escola, Joao Paulo |
title_short |
Wavelet Transform Applied to Coffee Entomology |
title_full |
Wavelet Transform Applied to Coffee Entomology |
title_fullStr |
Wavelet Transform Applied to Coffee Entomology |
title_full_unstemmed |
Wavelet Transform Applied to Coffee Entomology |
title_sort |
Wavelet Transform Applied to Coffee Entomology |
author |
Lemos Escola, Joao Paulo |
author_facet |
Lemos Escola, Joao Paulo Da Silva, Ivan Nunes Guido, Rodrigo Capobianco [UNESP] Fonseca, Everthon Silva |
author_role |
author |
author2 |
Da Silva, Ivan Nunes Guido, Rodrigo Capobianco [UNESP] Fonseca, Everthon Silva |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) Universidade Estadual Paulista (UNESP) Instituto Federal de São Paulo |
dc.contributor.author.fl_str_mv |
Lemos Escola, Joao Paulo Da Silva, Ivan Nunes Guido, Rodrigo Capobianco [UNESP] Fonseca, Everthon Silva |
description |
In this work, the design and development of a computational algorithm to assist in the management of insect pests in coffee plantations are presented, particularly for detecting the presence of cicadas. Acoustic signals, previously captured, are submitted to the proposed system which reads the raw data, converts them to the wavelet domain and groups them together based on the Bark Scale. Then, Paraconsistent Characteristics Analysis, appearing as a technique recently presented in the scientific literature and which had not yet been used for this purpose, serves as a basis for selecting the best filter banks so that they can be later delivered to a Support Vector Machine (SVM), responsible for the final step of signal identification. The accuracy of 100% was achieved in most of the 3600 tests performed, proving the viability of the implemented strategy, which has become minimally complex due to the optimization provided by the paraconsistent methodology. Finally, a prototype in the scope of Internet of Things is described to serve as a possibility of implantation in the field. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 2022-05-01T12:40:50Z 2022-05-01T12:40:50Z |
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 |
http://dx.doi.org/10.1109/SPSympo51155.2020.9593404 2021 Signal Processing Symposium, SPSympo 2021, p. 58-64. http://hdl.handle.net/11449/234042 10.1109/SPSympo51155.2020.9593404 2-s2.0-85123353947 |
url |
http://dx.doi.org/10.1109/SPSympo51155.2020.9593404 http://hdl.handle.net/11449/234042 |
identifier_str_mv |
2021 Signal Processing Symposium, SPSympo 2021, p. 58-64. 10.1109/SPSympo51155.2020.9593404 2-s2.0-85123353947 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2021 Signal Processing Symposium, SPSympo 2021 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
58-64 |
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_ |
1808129579225710592 |