Estimatição de eclosão em imagens de ovos do carrapato bovino baseado em redes neurais convolucionais
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da UFMA |
Texto Completo: | https://tedebc.ufma.br/jspui/handle/tede/tede/4936 |
Resumo: | The cattle tick is an ectoparasite that causes losses of more than 3 billion dollars annually in cattle farming in Brazil, either by the transmission of diseases or by the reduction in the quality of the derived products. The use of chemical acaricides is the most common form of control. To choose the most effective acaricide, tests are carried out in the laboratory. Engorged females are used as samples and immersed in commercial solutions of different chemical classes. The parameters evaluated include weight of females, egg laying and the percentage of hatchability of larvae, which is determined by counting fertile and infertile eggs. This counting process is usually performed manually, which consumes a lot of time and is repetitive and tiring, and therefore, this dissertation aims to automate this procedure. In this context, a computational method is proposed to account for and estimate the percentage of hatchability based on image processing and deep learning techniques, which follows the flow: pre-pocessing, slide extraction, egg segmentation; classification and counting of eggs. The method proposes a convolutional neural network architecture with the inclusion of soft attention mechanisms, called DenseNetSA, which was compared with other network architectures. The method achieves promising results with the DenseNetSA network for the group with 6 images, with 98% of fertile eggs and 88.67% of infertile eggs correctly classified. For the group with 3 images, 98% of the fertile eggs and 90.3% of the infertile eggs were correctly classified. The percentage of hatching presented the following values: 96.35% ± 3.33; 95.98% ± 3.5; 0.0% ± 0.0 for the groups with three images in the Piracanjuba, Desterro and Barbalha populations, respectively; and 94.41% ± 3.84; 95.93% ± 2.36; 0.0% ± 0.0 for the groups with six images in the Piracanjuba, Desterro and Barbalha populations, respectively. There was no statistical difference between the evaluated methods. The automatic method for predicting the hatching percentage of R. microplus larvae was validated and proved to be effective, with considerable reduction in time to obtain results. |
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SILVA, Aristófanes Corrêahttp://lattes.cnpq.br/2446301582459104COSTA JUNIOR, Livio Martinshttp://lattes.cnpq.br/6651961821189728SILVA, Aristófanes Corrêahttp://lattes.cnpq.br/2446301582459104COSTA JÚNIOR, Livio Martinshttp://lattes.cnpq.br/6651961821189728CAVALCANTE, André Borgeshttp://lattes.cnpq.br/3885279033465023LOPES, Welber Daniel Zanettihttp://lattes.cnpq.br/4480577038117234https://lattes.cnpq.br/8519873144812018SANTOS, Igor Silva2023-09-06T16:43:29Z2023-07-07SANTOS, Igor Silva. Estimatição de eclosão em imagens de ovos do carrapato bovino baseado em redes neurais convolucionais. 2023. 65 f. Dissertação (Programa de Pós-Graduação em Engenharia de Eletricidade/CCET) - Universidade Federal do Maranhão, São Luís, 2023.https://tedebc.ufma.br/jspui/handle/tede/tede/4936The cattle tick is an ectoparasite that causes losses of more than 3 billion dollars annually in cattle farming in Brazil, either by the transmission of diseases or by the reduction in the quality of the derived products. The use of chemical acaricides is the most common form of control. To choose the most effective acaricide, tests are carried out in the laboratory. Engorged females are used as samples and immersed in commercial solutions of different chemical classes. The parameters evaluated include weight of females, egg laying and the percentage of hatchability of larvae, which is determined by counting fertile and infertile eggs. This counting process is usually performed manually, which consumes a lot of time and is repetitive and tiring, and therefore, this dissertation aims to automate this procedure. In this context, a computational method is proposed to account for and estimate the percentage of hatchability based on image processing and deep learning techniques, which follows the flow: pre-pocessing, slide extraction, egg segmentation; classification and counting of eggs. The method proposes a convolutional neural network architecture with the inclusion of soft attention mechanisms, called DenseNetSA, which was compared with other network architectures. The method achieves promising results with the DenseNetSA network for the group with 6 images, with 98% of fertile eggs and 88.67% of infertile eggs correctly classified. For the group with 3 images, 98% of the fertile eggs and 90.3% of the infertile eggs were correctly classified. The percentage of hatching presented the following values: 96.35% ± 3.33; 95.98% ± 3.5; 0.0% ± 0.0 for the groups with three images in the Piracanjuba, Desterro and Barbalha populations, respectively; and 94.41% ± 3.84; 95.93% ± 2.36; 0.0% ± 0.0 for the groups with six images in the Piracanjuba, Desterro and Barbalha populations, respectively. There was no statistical difference between the evaluated methods. The automatic method for predicting the hatching percentage of R. microplus larvae was validated and proved to be effective, with considerable reduction in time to obtain results.O carrapato bovino é um ectoparasito que acarreta perdas acima de três bilhões de dólares anualmente na bovinocultura Brasileira, seja pela transmissão de doenças ou pela redução na qualidade nos produtos derivados. O uso de carrapaticidas químicos é a forma mais comum de controle. Para escolher o carrapaticida mais eficaz, testes são realizados em laboratório. Fêmeas ingurgitadas são utilizadas como amostras e imersas em soluções comerciais de carrapaticidas de diferentes classes químicas. Os parâmetros avaliados incluem peso das fêmeas, postura de ovos e a percentagem de eclodibilidade das larvas, que é determinada por contagem de ovos não eclodidos (inférteis) e larvas (férteis) após aproximadamente 45 dias do início. Esse processo de contagem é normalmente realizado manualmente, o que consome muito tempo e é repetitivo e cansativo, e por isso, a presente dissertação tem como objetivo a automação desse procedimento. Neste contexto propõe-se um método computacional para contabilizar e estimar o percentual de eclodibilidade baseado em técnicas de processamento de imagens e aprendizado de profundo, que segue o fluxo: pré-processamento, segmentação inicial, segmentação dos ovos; classificação e contagem dos ovos. O método propõe uma arquitetura de rede neural convolucional com a inclusão do mecanismo de atenção suave, denominado de DenseNetSA, que foi comparada com outras arquiteturas de rede. O método alcança resultados promissores com a rede DenseNetSA para o grupo com 6 imagens, com 98% dos ovos férteis e 88,67% dos ovos inférteis classificados corretamente. Para o grupo com 3 imagens, 98% dos ovos férteis e 90,3% dos ovos inférteis foram classificados corretamente. O percentual de eclosão apresentarou os seguintes valores: 96,35% ± 3,33; 95,98% ± 3,5; 0,0% ± 0,0 para os grupos com 3 imagens nas populações Piracanjuba, Desterro e Barbalha, respectivamente; e 94,41% ± 3,84; 95,93% ± 2,36; 0,0% ± 0,0 para os grupos com 6 imagens nas populações Piracanjuba, Desterro e Barbalha, respectivamente. Não houve diferença estatística entre os métodos avaliados. O método automático de predição da porcentagem de eclosão de larvas de R. microplus foi validado e mostrou-se eficaz, com considerável redução no tempo de obtenção dos resultados.Submitted by Jonathan Sousa de Almeida (jonathan.sousa@ufma.br) on 2023-09-06T16:43:29Z No. of bitstreams: 1 IGORSILVASANTOS.pdf: 11949621 bytes, checksum: 84a005d0b503ce22b93bbb8944939321 (MD5)Made available in DSpace on 2023-09-06T16:43:29Z (GMT). No. of bitstreams: 1 IGORSILVASANTOS.pdf: 11949621 bytes, checksum: 84a005d0b503ce22b93bbb8944939321 (MD5) Previous issue date: 2023-07-07FAPEMAapplication/pdfporUniversidade Federal do MaranhãoPROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCETUFMABrasilDEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCETaprendizagem profunda;ovos férteis;processamento de imagem;carrapato;atenção suave;deep learning;fertile eggs;image processing;tick;gentle attention.Ciência da ComputaçãoEngenharia AgrícolaEstimatição de eclosão em imagens de ovos do carrapato bovino baseado em redes neurais convolucionaisHatching estimation in bovine tick egg images based on convolutional neural networksinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UFMAinstname:Universidade Federal do Maranhão (UFMA)instacron:UFMAORIGINALIGORSILVASANTOS.pdfIGORSILVASANTOS.pdfapplication/pdf11949621http://tedebc.ufma.br:8080/bitstream/tede/4936/2/IGORSILVASANTOS.pdf84a005d0b503ce22b93bbb8944939321MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82255http://tedebc.ufma.br:8080/bitstream/tede/4936/1/license.txt97eeade1fce43278e63fe063657f8083MD51tede/49362023-09-06 13:43:29.877oai:tede2: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Biblioteca Digital de Teses e Dissertaçõeshttps://tedebc.ufma.br/jspui/PUBhttp://tedebc.ufma.br:8080/oai/requestrepositorio@ufma.br||repositorio@ufma.bropendoar:21312023-09-06T16:43:29Biblioteca Digital de Teses e Dissertações da UFMA - Universidade Federal do Maranhão (UFMA)false |
dc.title.por.fl_str_mv |
Estimatição de eclosão em imagens de ovos do carrapato bovino baseado em redes neurais convolucionais |
dc.title.alternative.eng.fl_str_mv |
Hatching estimation in bovine tick egg images based on convolutional neural networks |
title |
Estimatição de eclosão em imagens de ovos do carrapato bovino baseado em redes neurais convolucionais |
spellingShingle |
Estimatição de eclosão em imagens de ovos do carrapato bovino baseado em redes neurais convolucionais SANTOS, Igor Silva aprendizagem profunda; ovos férteis; processamento de imagem; carrapato; atenção suave; deep learning; fertile eggs; image processing; tick; gentle attention. Ciência da Computação Engenharia Agrícola |
title_short |
Estimatição de eclosão em imagens de ovos do carrapato bovino baseado em redes neurais convolucionais |
title_full |
Estimatição de eclosão em imagens de ovos do carrapato bovino baseado em redes neurais convolucionais |
title_fullStr |
Estimatição de eclosão em imagens de ovos do carrapato bovino baseado em redes neurais convolucionais |
title_full_unstemmed |
Estimatição de eclosão em imagens de ovos do carrapato bovino baseado em redes neurais convolucionais |
title_sort |
Estimatição de eclosão em imagens de ovos do carrapato bovino baseado em redes neurais convolucionais |
author |
SANTOS, Igor Silva |
author_facet |
SANTOS, Igor Silva |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
SILVA, Aristófanes Corrêa |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/2446301582459104 |
dc.contributor.advisor-co1.fl_str_mv |
COSTA JUNIOR, Livio Martins |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/6651961821189728 |
dc.contributor.referee1.fl_str_mv |
SILVA, Aristófanes Corrêa |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/2446301582459104 |
dc.contributor.referee2.fl_str_mv |
COSTA JÚNIOR, Livio Martins |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/6651961821189728 |
dc.contributor.referee3.fl_str_mv |
CAVALCANTE, André Borges |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/3885279033465023 |
dc.contributor.referee4.fl_str_mv |
LOPES, Welber Daniel Zanetti |
dc.contributor.referee4Lattes.fl_str_mv |
http://lattes.cnpq.br/4480577038117234 |
dc.contributor.authorLattes.fl_str_mv |
https://lattes.cnpq.br/8519873144812018 |
dc.contributor.author.fl_str_mv |
SANTOS, Igor Silva |
contributor_str_mv |
SILVA, Aristófanes Corrêa COSTA JUNIOR, Livio Martins SILVA, Aristófanes Corrêa COSTA JÚNIOR, Livio Martins CAVALCANTE, André Borges LOPES, Welber Daniel Zanetti |
dc.subject.por.fl_str_mv |
aprendizagem profunda; ovos férteis; processamento de imagem; carrapato; atenção suave; |
topic |
aprendizagem profunda; ovos férteis; processamento de imagem; carrapato; atenção suave; deep learning; fertile eggs; image processing; tick; gentle attention. Ciência da Computação Engenharia Agrícola |
dc.subject.eng.fl_str_mv |
deep learning; fertile eggs; image processing; tick; gentle attention. |
dc.subject.cnpq.fl_str_mv |
Ciência da Computação Engenharia Agrícola |
description |
The cattle tick is an ectoparasite that causes losses of more than 3 billion dollars annually in cattle farming in Brazil, either by the transmission of diseases or by the reduction in the quality of the derived products. The use of chemical acaricides is the most common form of control. To choose the most effective acaricide, tests are carried out in the laboratory. Engorged females are used as samples and immersed in commercial solutions of different chemical classes. The parameters evaluated include weight of females, egg laying and the percentage of hatchability of larvae, which is determined by counting fertile and infertile eggs. This counting process is usually performed manually, which consumes a lot of time and is repetitive and tiring, and therefore, this dissertation aims to automate this procedure. In this context, a computational method is proposed to account for and estimate the percentage of hatchability based on image processing and deep learning techniques, which follows the flow: pre-pocessing, slide extraction, egg segmentation; classification and counting of eggs. The method proposes a convolutional neural network architecture with the inclusion of soft attention mechanisms, called DenseNetSA, which was compared with other network architectures. The method achieves promising results with the DenseNetSA network for the group with 6 images, with 98% of fertile eggs and 88.67% of infertile eggs correctly classified. For the group with 3 images, 98% of the fertile eggs and 90.3% of the infertile eggs were correctly classified. The percentage of hatching presented the following values: 96.35% ± 3.33; 95.98% ± 3.5; 0.0% ± 0.0 for the groups with three images in the Piracanjuba, Desterro and Barbalha populations, respectively; and 94.41% ± 3.84; 95.93% ± 2.36; 0.0% ± 0.0 for the groups with six images in the Piracanjuba, Desterro and Barbalha populations, respectively. There was no statistical difference between the evaluated methods. The automatic method for predicting the hatching percentage of R. microplus larvae was validated and proved to be effective, with considerable reduction in time to obtain results. |
publishDate |
2023 |
dc.date.accessioned.fl_str_mv |
2023-09-06T16:43:29Z |
dc.date.issued.fl_str_mv |
2023-07-07 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
SANTOS, Igor Silva. Estimatição de eclosão em imagens de ovos do carrapato bovino baseado em redes neurais convolucionais. 2023. 65 f. Dissertação (Programa de Pós-Graduação em Engenharia de Eletricidade/CCET) - Universidade Federal do Maranhão, São Luís, 2023. |
dc.identifier.uri.fl_str_mv |
https://tedebc.ufma.br/jspui/handle/tede/tede/4936 |
identifier_str_mv |
SANTOS, Igor Silva. Estimatição de eclosão em imagens de ovos do carrapato bovino baseado em redes neurais convolucionais. 2023. 65 f. Dissertação (Programa de Pós-Graduação em Engenharia de Eletricidade/CCET) - Universidade Federal do Maranhão, São Luís, 2023. |
url |
https://tedebc.ufma.br/jspui/handle/tede/tede/4936 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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dc.publisher.none.fl_str_mv |
Universidade Federal do Maranhão |
dc.publisher.program.fl_str_mv |
PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET |
dc.publisher.initials.fl_str_mv |
UFMA |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET |
publisher.none.fl_str_mv |
Universidade Federal do Maranhão |
dc.source.none.fl_str_mv |
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Biblioteca Digital de Teses e Dissertações da UFMA |
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