Classifica??o do inseto Diaphorina citri utilizando Deep Learning
Ano de defesa: | 2018 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Estadual de Feira de Santana
|
Programa de Pós-Graduação: |
Mestrado em Computa??o Aplicada
|
Departamento: |
DEPARTAMENTO DE CI?NCIAS EXATAS
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://tede2.uefs.br:8080/handle/tede/1343 |
Resumo: | Brazil is one of the main producers of orange juice, exporting 98% of its production.Phytosanitary problems cause loss of production and difficulty in exporting. Among existing pests is the industry?s biggest concern that Huanglongbing, also known as Greening. This disease reduces the quality of the fruit, disrupts the developmentof the plant and reduces its productivity. The main spreader of the disease-causing bacteria is the Diaphorina citri, which is 2 to 3 mm in length. For the control of the problem, one method is to capture from yellow traps and the count of the insects that insects to later adjust the dosage of the insecticides to be applied. This way of monitoring is an important component in the prevention, the detection and counting of the insect that causes the disease is carried out manually and this process is determinant for more effective insecticide applications. This research had as general objective the use of the methodology of Deep Learning with Neural Convolutional Networks (CNN) in the classification of the insect Diaphorina citri in digitized images of adhesive traps, in order to streamline the identification process and improve the accuracy results in the recognition of the insect. To do this, it was necessary to create a database with the images of the traps after digitized and to analyze the results obtained in the classification of Diaphorina citri using models of distinct architectures with deep learning approach. For automated classification, three architectures of Neural Convolutional Networks (CNN) were tried. After evaluation and application of statistical tests to compare the results of the LeNet, AlexNet, Inception architectures, the Inception model applied to the set of test samples for generalization of the model presented an average accuracy of 99.51% accuracy inthe crossvalidation and 99.37% in the validation with the final test set in the classification of the insect Diaphorina citri. |
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Angelo, Michele F?lvia26554093885http://lattes.cnpq.br/603227384984728551395126291http://lattes.cnpq.br/4047734305944217Pierre J?nior, M?rio L?cio Gomes de Queiroz2022-04-19T20:58:45Z2018-09-03PIERRE J?NIOR, M?rio L?cio Gomes de Queiroz. Classifica??o do inseto Diaphorina citri utilizando Deep Learning. 2018. 69 f. Disserta??o (Mestrado em Computa??o Aplicada)- Universidade Estadual de Feira de Santana, Feira de Santana, 2018.http://tede2.uefs.br:8080/handle/tede/1343Brazil is one of the main producers of orange juice, exporting 98% of its production.Phytosanitary problems cause loss of production and difficulty in exporting. Among existing pests is the industry?s biggest concern that Huanglongbing, also known as Greening. This disease reduces the quality of the fruit, disrupts the developmentof the plant and reduces its productivity. The main spreader of the disease-causing bacteria is the Diaphorina citri, which is 2 to 3 mm in length. For the control of the problem, one method is to capture from yellow traps and the count of the insects that insects to later adjust the dosage of the insecticides to be applied. This way of monitoring is an important component in the prevention, the detection and counting of the insect that causes the disease is carried out manually and this process is determinant for more effective insecticide applications. This research had as general objective the use of the methodology of Deep Learning with Neural Convolutional Networks (CNN) in the classification of the insect Diaphorina citri in digitized images of adhesive traps, in order to streamline the identification process and improve the accuracy results in the recognition of the insect. To do this, it was necessary to create a database with the images of the traps after digitized and to analyze the results obtained in the classification of Diaphorina citri using models of distinct architectures with deep learning approach. For automated classification, three architectures of Neural Convolutional Networks (CNN) were tried. After evaluation and application of statistical tests to compare the results of the LeNet, AlexNet, Inception architectures, the Inception model applied to the set of test samples for generalization of the model presented an average accuracy of 99.51% accuracy inthe crossvalidation and 99.37% in the validation with the final test set in the classification of the insect Diaphorina citri.O Brasil ? um dos principais produtores de suco de laranja, exportando 98% do que produz. E problemas fitossanit?rios causam perda na produ??o e dificuldade de exporta??o. Dentre as pragas existentes est? a Huanglongbing, tamb?m conhecida por Greening. Esta doen?a reduz a qualidade do fruto, atrapalha o desenvolvimento da planta e reduz sua produtividade. O principal inseto propagador da bact?ria causadora da doen?a? o psil?deo Diaphorina citri, que tem 2 a 3 mm de comprimento. Para o controle do problema, um m?todo usado consiste na captura com armadilhas amarelas e contagem dos insetos para ajuste posterior da dosagem dos inseticidas a serem aplicados. Essa forma de monitoramento ? um componente importante na preven??o, na detec??o e contagem do inseto causador da doen?a, e ? realizada de maneira manual sendo esse processo determinante para aplica??o mais efetivas de inseticidas. Esta pesquisa, teve como objetivo geral o uso da metodologia de Aprendizado Profundo (Deep Learning) com Redes Convolucionais Neurais (CNN) na classifica??o do psil?deo Diaphorina citri em imagens digitalizadas de armadilhas adesivas, como forma de agilizar o processo de identifica??o e melhorar os resultados de precis?o no reconhecimento do psil?deo. Para isso, foi necess?rio criar um banco de dados com as imagens das armadilhas ap?s digitalizadas e analisar os resultados obtidos na classifica??o do Diaphorina citri utilizando modelos de arquiteturas distintos com abordagem de aprendizado profundo. Para a classifica??o automatizada, foram experimentadas tr?s arquiteturas de Redes Convolucionais Neurais (CNN). Ap?s avalia??o e aplica??o de testes estat?sticos para comparar os resultados das arquiteturas LeNet, AlexNet, Inception, o modelo Inception aplicado ao conjunto de amostras de teste para generaliza??o do modelo apresentou uma m?dia de acur?cia de 99.51% na valida??o cruzada e 99.37% na valida??o com o conjunto de teste final na classifica??o do inseto Diaphorina citri.Submitted by Ricardo Cedraz Duque Moliterno (ricardo.moliterno@uefs.br) on 2022-04-19T20:58:45Z No. of bitstreams: 1 01_Dissertac_a_oPGCA_vFinal_CD.pdf: 7825498 bytes, checksum: be10ffb1bdb8c47b14eff89c7c71816d (MD5)Made available in DSpace on 2022-04-19T20:58:45Z (GMT). No. of bitstreams: 1 01_Dissertac_a_oPGCA_vFinal_CD.pdf: 7825498 bytes, checksum: be10ffb1bdb8c47b14eff89c7c71816d (MD5) Previous issue date: 2018-09-03application/pdfporUniversidade Estadual de Feira de SantanaMestrado em Computa??o AplicadaUEFSBrasilDEPARTAMENTO DE CI?NCIAS EXATASDeep LearningRedes Convolucionais NeuraisHuanglongbinDiaphorina citriDeep LearningConvolutioin Neural NetworkHuanglongbingDiaphorina citriCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAOClassifica??o do inseto Diaphorina citri utilizando Deep Learninginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis303317282311144204600600600-54868328166115062113671711205811204509info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UEFSinstname:Universidade Estadual de Feira de Santana (UEFS)instacron:UEFSORIGINAL01_Dissertac_a_oPGCA_vFinal_CD.pdf01_Dissertac_a_oPGCA_vFinal_CD.pdfapplication/pdf7825498http://tede2.uefs.br:8080/bitstream/tede/1343/2/01_Dissertac_a_oPGCA_vFinal_CD.pdfbe10ffb1bdb8c47b14eff89c7c71816dMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82089http://tede2.uefs.br:8080/bitstream/tede/1343/1/license.txt7b5ba3d2445355f386edab96125d42b7MD51tede/13432022-04-19 17:58:45.656oai:tede2.uefs.br:8080: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Biblioteca Digital de Teses e Dissertaçõeshttp://tede2.uefs.br:8080/PUBhttp://tede2.uefs.br:8080/oai/requestbcuefs@uefs.br|| bcref@uefs.br||bcuefs@uefs.bropendoar:2022-04-19T20:58:45Biblioteca Digital de Teses e Dissertações da UEFS - Universidade Estadual de Feira de Santana (UEFS)false |
dc.title.por.fl_str_mv |
Classifica??o do inseto Diaphorina citri utilizando Deep Learning |
title |
Classifica??o do inseto Diaphorina citri utilizando Deep Learning |
spellingShingle |
Classifica??o do inseto Diaphorina citri utilizando Deep Learning Pierre J?nior, M?rio L?cio Gomes de Queiroz Deep Learning Redes Convolucionais Neurais Huanglongbin Diaphorina citri Deep Learning Convolutioin Neural Network Huanglongbing Diaphorina citri CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
title_short |
Classifica??o do inseto Diaphorina citri utilizando Deep Learning |
title_full |
Classifica??o do inseto Diaphorina citri utilizando Deep Learning |
title_fullStr |
Classifica??o do inseto Diaphorina citri utilizando Deep Learning |
title_full_unstemmed |
Classifica??o do inseto Diaphorina citri utilizando Deep Learning |
title_sort |
Classifica??o do inseto Diaphorina citri utilizando Deep Learning |
author |
Pierre J?nior, M?rio L?cio Gomes de Queiroz |
author_facet |
Pierre J?nior, M?rio L?cio Gomes de Queiroz |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Angelo, Michele F?lvia |
dc.contributor.advisor1ID.fl_str_mv |
26554093885 |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/6032273849847285 |
dc.contributor.authorID.fl_str_mv |
51395126291 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/4047734305944217 |
dc.contributor.author.fl_str_mv |
Pierre J?nior, M?rio L?cio Gomes de Queiroz |
contributor_str_mv |
Angelo, Michele F?lvia |
dc.subject.por.fl_str_mv |
Deep Learning Redes Convolucionais Neurais Huanglongbin Diaphorina citri |
topic |
Deep Learning Redes Convolucionais Neurais Huanglongbin Diaphorina citri Deep Learning Convolutioin Neural Network Huanglongbing Diaphorina citri CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
dc.subject.eng.fl_str_mv |
Deep Learning Convolutioin Neural Network Huanglongbing Diaphorina citri |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
description |
Brazil is one of the main producers of orange juice, exporting 98% of its production.Phytosanitary problems cause loss of production and difficulty in exporting. Among existing pests is the industry?s biggest concern that Huanglongbing, also known as Greening. This disease reduces the quality of the fruit, disrupts the developmentof the plant and reduces its productivity. The main spreader of the disease-causing bacteria is the Diaphorina citri, which is 2 to 3 mm in length. For the control of the problem, one method is to capture from yellow traps and the count of the insects that insects to later adjust the dosage of the insecticides to be applied. This way of monitoring is an important component in the prevention, the detection and counting of the insect that causes the disease is carried out manually and this process is determinant for more effective insecticide applications. This research had as general objective the use of the methodology of Deep Learning with Neural Convolutional Networks (CNN) in the classification of the insect Diaphorina citri in digitized images of adhesive traps, in order to streamline the identification process and improve the accuracy results in the recognition of the insect. To do this, it was necessary to create a database with the images of the traps after digitized and to analyze the results obtained in the classification of Diaphorina citri using models of distinct architectures with deep learning approach. For automated classification, three architectures of Neural Convolutional Networks (CNN) were tried. After evaluation and application of statistical tests to compare the results of the LeNet, AlexNet, Inception architectures, the Inception model applied to the set of test samples for generalization of the model presented an average accuracy of 99.51% accuracy inthe crossvalidation and 99.37% in the validation with the final test set in the classification of the insect Diaphorina citri. |
publishDate |
2018 |
dc.date.issued.fl_str_mv |
2018-09-03 |
dc.date.accessioned.fl_str_mv |
2022-04-19T20:58:45Z |
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 |
PIERRE J?NIOR, M?rio L?cio Gomes de Queiroz. Classifica??o do inseto Diaphorina citri utilizando Deep Learning. 2018. 69 f. Disserta??o (Mestrado em Computa??o Aplicada)- Universidade Estadual de Feira de Santana, Feira de Santana, 2018. |
dc.identifier.uri.fl_str_mv |
http://tede2.uefs.br:8080/handle/tede/1343 |
identifier_str_mv |
PIERRE J?NIOR, M?rio L?cio Gomes de Queiroz. Classifica??o do inseto Diaphorina citri utilizando Deep Learning. 2018. 69 f. Disserta??o (Mestrado em Computa??o Aplicada)- Universidade Estadual de Feira de Santana, Feira de Santana, 2018. |
url |
http://tede2.uefs.br:8080/handle/tede/1343 |
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por |
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por |
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600 600 600 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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Universidade Estadual de Feira de Santana |
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Mestrado em Computa??o Aplicada |
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UEFS |
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