Empirical mode decomposition applied to acoustic detection of a cicadid pest
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.compag.2022.107181 http://hdl.handle.net/11449/242006 |
Resumo: | The sounds emitted by various insect species are highly specific and, thus, can be used as a way to acoustically characterize them. Consequently, acoustic insect detection has been widely studied by the scientific community in the field of pattern recognition. In Brazil, the cicada species Quesada gigas is considered a pest in coffee plantations, because the insects feed on the sap of the plants and can cause losses to farmers in mass attacks. Based on the fact that the most striking feature of cicadas is the emission of sounds for breeding purposes, this paper presents an alternative algorithm for acoustic detection of cicadas. The algorithm combines sound feature extraction with feature analysis based on Empirical Mode Decomposition (EMD) and Paraconsistent Feature Engineering (PFE), respectively, followed by a classification step based on a Support Vector Machine (SVM). Specifically, a study on the influence of eight EMD stopping criteria on the classification of sounds is presented. The results show that the proposed methodology can obtain accuracy values above 98% considering the Energy Difference Tracking (EDT) stopping criterion, vectors with 18 features and at least 46% of the vectors for SVM training. In the computational cost aspect, the stopping criterion Standard Deviation (SD) stands out, providing accuracy values above 96.67% for vectors with only two features. These results show that this study is feasible for Internet of Things applications, favoring the development of detection devices for field use with long-lasting autonomy. Technologies like these can enable the implementation of more and more daring projects involving Smart Farms and e-waste, aiming to reduce impacts to the environment. Suggestions for future work based on the PFE are also presented. |
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Empirical mode decomposition applied to acoustic detection of a cicadid pestCicadaEmpirical Mode DecompositionEvent classificationMonitoring systemParaconsistent Feature EngineeringSmart FarmsThe sounds emitted by various insect species are highly specific and, thus, can be used as a way to acoustically characterize them. Consequently, acoustic insect detection has been widely studied by the scientific community in the field of pattern recognition. In Brazil, the cicada species Quesada gigas is considered a pest in coffee plantations, because the insects feed on the sap of the plants and can cause losses to farmers in mass attacks. Based on the fact that the most striking feature of cicadas is the emission of sounds for breeding purposes, this paper presents an alternative algorithm for acoustic detection of cicadas. The algorithm combines sound feature extraction with feature analysis based on Empirical Mode Decomposition (EMD) and Paraconsistent Feature Engineering (PFE), respectively, followed by a classification step based on a Support Vector Machine (SVM). Specifically, a study on the influence of eight EMD stopping criteria on the classification of sounds is presented. The results show that the proposed methodology can obtain accuracy values above 98% considering the Energy Difference Tracking (EDT) stopping criterion, vectors with 18 features and at least 46% of the vectors for SVM training. In the computational cost aspect, the stopping criterion Standard Deviation (SD) stands out, providing accuracy values above 96.67% for vectors with only two features. These results show that this study is feasible for Internet of Things applications, favoring the development of detection devices for field use with long-lasting autonomy. Technologies like these can enable the implementation of more and more daring projects involving Smart Farms and e-waste, aiming to reduce impacts to the environment. Suggestions for future work based on the PFE are also presented.Instituto Federal de Goiás, DAAII, Matemática, Rua 75, 46Universidade Federal de Goiás, EMC, Av. Universitária, 1488Instituto Federal de São Paulo, Av. C-1, 250Instituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo, 2265Universidade Estadual de Goiás, Av. R2, Qd.01, Jardim Novo Horizonte IIInstituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo, 2265Instituto Federal de GoiásUniversidade Federal de Goiás (UFG)Instituto Federal de São PauloUniversidade Estadual Paulista (UNESP)Universidade Estadual de GoiásSouza, Uender Barbosa deEscola, João Paulo LemosMaccagnan, Douglas Henrique BotturaBrito, Leonardo da CunhaGuido, Rodrigo Capobianco [UNESP]2023-03-02T06:29:57Z2023-03-02T06:29:57Z2022-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.compag.2022.107181Computers and Electronics in Agriculture, v. 199.0168-1699http://hdl.handle.net/11449/24200610.1016/j.compag.2022.1071812-s2.0-85133419563Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputers and Electronics in Agricultureinfo:eu-repo/semantics/openAccess2023-03-02T06:29:57Zoai:repositorio.unesp.br:11449/242006Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:19:04.811764Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Empirical mode decomposition applied to acoustic detection of a cicadid pest |
title |
Empirical mode decomposition applied to acoustic detection of a cicadid pest |
spellingShingle |
Empirical mode decomposition applied to acoustic detection of a cicadid pest Souza, Uender Barbosa de Cicada Empirical Mode Decomposition Event classification Monitoring system Paraconsistent Feature Engineering Smart Farms |
title_short |
Empirical mode decomposition applied to acoustic detection of a cicadid pest |
title_full |
Empirical mode decomposition applied to acoustic detection of a cicadid pest |
title_fullStr |
Empirical mode decomposition applied to acoustic detection of a cicadid pest |
title_full_unstemmed |
Empirical mode decomposition applied to acoustic detection of a cicadid pest |
title_sort |
Empirical mode decomposition applied to acoustic detection of a cicadid pest |
author |
Souza, Uender Barbosa de |
author_facet |
Souza, Uender Barbosa de Escola, João Paulo Lemos Maccagnan, Douglas Henrique Bottura Brito, Leonardo da Cunha Guido, Rodrigo Capobianco [UNESP] |
author_role |
author |
author2 |
Escola, João Paulo Lemos Maccagnan, Douglas Henrique Bottura Brito, Leonardo da Cunha Guido, Rodrigo Capobianco [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Instituto Federal de Goiás Universidade Federal de Goiás (UFG) Instituto Federal de São Paulo Universidade Estadual Paulista (UNESP) Universidade Estadual de Goiás |
dc.contributor.author.fl_str_mv |
Souza, Uender Barbosa de Escola, João Paulo Lemos Maccagnan, Douglas Henrique Bottura Brito, Leonardo da Cunha Guido, Rodrigo Capobianco [UNESP] |
dc.subject.por.fl_str_mv |
Cicada Empirical Mode Decomposition Event classification Monitoring system Paraconsistent Feature Engineering Smart Farms |
topic |
Cicada Empirical Mode Decomposition Event classification Monitoring system Paraconsistent Feature Engineering Smart Farms |
description |
The sounds emitted by various insect species are highly specific and, thus, can be used as a way to acoustically characterize them. Consequently, acoustic insect detection has been widely studied by the scientific community in the field of pattern recognition. In Brazil, the cicada species Quesada gigas is considered a pest in coffee plantations, because the insects feed on the sap of the plants and can cause losses to farmers in mass attacks. Based on the fact that the most striking feature of cicadas is the emission of sounds for breeding purposes, this paper presents an alternative algorithm for acoustic detection of cicadas. The algorithm combines sound feature extraction with feature analysis based on Empirical Mode Decomposition (EMD) and Paraconsistent Feature Engineering (PFE), respectively, followed by a classification step based on a Support Vector Machine (SVM). Specifically, a study on the influence of eight EMD stopping criteria on the classification of sounds is presented. The results show that the proposed methodology can obtain accuracy values above 98% considering the Energy Difference Tracking (EDT) stopping criterion, vectors with 18 features and at least 46% of the vectors for SVM training. In the computational cost aspect, the stopping criterion Standard Deviation (SD) stands out, providing accuracy values above 96.67% for vectors with only two features. These results show that this study is feasible for Internet of Things applications, favoring the development of detection devices for field use with long-lasting autonomy. Technologies like these can enable the implementation of more and more daring projects involving Smart Farms and e-waste, aiming to reduce impacts to the environment. Suggestions for future work based on the PFE are also presented. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-08-01 2023-03-02T06:29:57Z 2023-03-02T06:29:57Z |
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/10.1016/j.compag.2022.107181 Computers and Electronics in Agriculture, v. 199. 0168-1699 http://hdl.handle.net/11449/242006 10.1016/j.compag.2022.107181 2-s2.0-85133419563 |
url |
http://dx.doi.org/10.1016/j.compag.2022.107181 http://hdl.handle.net/11449/242006 |
identifier_str_mv |
Computers and Electronics in Agriculture, v. 199. 0168-1699 10.1016/j.compag.2022.107181 2-s2.0-85133419563 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Computers and Electronics in Agriculture |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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 |
|
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1808129506808954880 |