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Registro Completo |
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Biblioteca(s): |
Embrapa Trigo. |
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Data corrente: |
21/12/2017 |
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Data da última atualização: |
16/01/2018 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
FERREIRA, J. R.; CAMILOTTI, G. A.; SOTO-GONZÁLES, H. H.; TURCHETTO, C.; CONSOLI, L.; TORRES, G. A. M.; DEUNER, C. C.; SCAGLIUSI, S. M. M.; FERNANDES, J. M. C. |
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Afiliação: |
JÉSSICA ROSSET FERREIRA, Pós-Graduação em Agronomia - UPF; GABRIELA ANDRIOLIO CAMILOTTI, Biologia - UPF; HEBERT HERNÁN SOTO-GONZÁLES, Pós-doutorando PNPD-CNPq; CAROLINE TURCHETTO, Pós-doutoranda PNPD-CNPq; LUCIANO CONSOLI, CNPT; GISELE ABIGAIL MONTAN TORRES, CNPT; CAROLINA CARDOSO DEUNER, Pós-Graduação em Agronomia - UPF; SANDRA MARIA MANSUR SCAGLIUSI, CNPT; JOSÉ MAURÍCIO CUNHA FERNANDES, CNPT. |
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Título: |
Identificação de QTLs associados com a resistência de trigo a Magnaporthe oryzae. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
In: MOSTRA DE INICIAÇÃO CIENTÍFICA, 12.; MOSTRA DE PÓS-GRADUAÇÃO DA EMBRAPA TRIGO, 9., 2017, Passo Fundo. Resumos... Passo Fundo: Embrapa Trigo, 2017. |
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Páginas: |
p. 50. |
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Idioma: |
Português |
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Palavras-Chave: |
Genotipagem; Mapeamento genético; Plant disease. |
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Thesagro: |
Brusone; Doença de planta; Trigo. |
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Thesaurus Nal: |
Chromosome mapping; Genotyping; Magnaporthe oryzae; Wheat. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/169528/1/2017MICp50.pdf
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Marc: |
LEADER 01031nam a2200325 a 4500
001 2083206
005 2018-01-16
008 2017 bl uuuu u00u1 u #d
100 1 $aFERREIRA, J. R.
245 $aIdentificação de QTLs associados com a resistência de trigo a Magnaporthe oryzae.$h[electronic resource]
260 $aIn: MOSTRA DE INICIAÇÃO CIENTÍFICA, 12.; MOSTRA DE PÓS-GRADUAÇÃO DA EMBRAPA TRIGO, 9., 2017, Passo Fundo. Resumos... Passo Fundo: Embrapa Trigo$c2017
300 $ap. 50.
650 $aChromosome mapping
650 $aGenotyping
650 $aMagnaporthe oryzae
650 $aWheat
650 $aBrusone
650 $aDoença de planta
650 $aTrigo
653 $aGenotipagem
653 $aMapeamento genético
653 $aPlant disease
700 1 $aCAMILOTTI, G. A.
700 1 $aSOTO-GONZÁLES, H. H.
700 1 $aTURCHETTO, C.
700 1 $aCONSOLI, L.
700 1 $aTORRES, G. A. M.
700 1 $aDEUNER, C. C.
700 1 $aSCAGLIUSI, S. M. M.
700 1 $aFERNANDES, J. M. C.
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Registro original: |
Embrapa Trigo (CNPT) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
30/11/2023 |
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Data da última atualização: |
30/11/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
SANTOS, I. S.; TAVARES, C. P.; KLAFKE, G. M.; RECK, J.; MONTEIRO, C. M. O.; PRATA, M. C. de A.; GOLO. P. S.; SILVA, A. C.; COSTA-JUNIOR, L. M. |
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Afiliação: |
IGOR S. SANTOS, UNIVERSIDADE FEDERAL DO MARANHÃO; CAIO P. TAVARES, UNIVERSIDADE FEDERAL DO MARANHÃO; GUILHERME M. KLAFKE, INSTITUTO DE PESQUISAS VETERINÁRIAS DESIDÉRIO FINAMOR; JOSÉ RECK, INSTITUTO DE PESQUISAS VETERINÁRIAS DESIDÉRIO FINAMOR; CAIO M. O. MONTEIRO, UNIVERSIDADE FEDERAL DE GOIÁS; MARCIA CRISTINA DE AZEVEDO PRATA, CNPGL; PATRÍCIA S. GOLO, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; ARISTOFANES C. SILVA, UNIVERSIDADE FEDERAL DO MARANHÃO; LIVIO M. COSTA-JUNIOR, UNIVERSIDADE FEDERAL DO MARANHÃO. |
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Título: |
Automatic method based on deep learning to identify and account Rhipicephalus microplus larval hatching. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Medical and Veterinary Entomology, v. 37, p. 665-674, 2023. |
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DOI: |
http://doi.org/10.1111/mve.12664 |
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Idioma: |
Inglês |
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Conteúdo: |
Reports of Rhipicephalus microplus resistant populations worldwide have increased extensively, making it difficult to control this ectoparasite. The adult immersion test, commonly used to screen for acaricide resistance, produces the results only after 40 days of the tick collection because it needs the eggs to be laid and larvae to hatch. The present study aims to develop an automatic method, based on deep learning, to predict the hatching of R. microplus larva based on egg morphology. Initially, the time course of embryonic development of tick eggs was performed to discriminate between viable and non-viable eggs. Secondly, using artificial intelligence deep learning techniques, a method was developed to classify and count the eggs. The larval hatching rate of three populations of R. microplus was evaluated for the software validation process. Groups of three and six images of eggs with 12 days of embryonic development were submitted to the software to predict the larval hatching percent automatically. The results obtained by the software were compared with the prediction results of the hatching percentage performed manually by the specialist and with the results of the hatching percentage of larvae obtained in the biological assay. The group with three images of each population submitted to the software for automatic prediction of the larval hatching percent presented mean values of 96.35% ± 3.33 (Piracanjuba population), 95.98% ± 3.5 (Desterro population) and 0.0% ± 0.0 (Barbalha population). For groups with six images, the values were 94.41% ± 3.84 (Piracanjuba population), 95.93% ± 2.36 (Desterro population) and 0.0% ± 0.0 (Barbalha population). Biological assays showed the following hatching percentage values: 98% ± 1.73 (Piracanjuba population); 96% ± 2.1 (Desterro population); and 0.14% ± 0.25 (Barbalha population). 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. MenosReports of Rhipicephalus microplus resistant populations worldwide have increased extensively, making it difficult to control this ectoparasite. The adult immersion test, commonly used to screen for acaricide resistance, produces the results only after 40 days of the tick collection because it needs the eggs to be laid and larvae to hatch. The present study aims to develop an automatic method, based on deep learning, to predict the hatching of R. microplus larva based on egg morphology. Initially, the time course of embryonic development of tick eggs was performed to discriminate between viable and non-viable eggs. Secondly, using artificial intelligence deep learning techniques, a method was developed to classify and count the eggs. The larval hatching rate of three populations of R. microplus was evaluated for the software validation process. Groups of three and six images of eggs with 12 days of embryonic development were submitted to the software to predict the larval hatching percent automatically. The results obtained by the software were compared with the prediction results of the hatching percentage performed manually by the specialist and with the results of the hatching percentage of larvae obtained in the biological assay. The group with three images of each population submitted to the software for automatic prediction of the larval hatching percent presented mean values of 96.35% ± 3.33 (Piracanjuba population), 95.98% ± 3.5 (Desterro population) and 0.0% ± 0.0 (... Mostrar Tudo |
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Palavras-Chave: |
Controle; Eclosão larval; Larval hatching. |
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Thesagro: |
Carrapato; Larva; Ovo; Resistência. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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Marc: |
LEADER 03010naa a2200313 a 4500
001 2158936
005 2023-11-30
008 2023 bl uuuu u00u1 u #d
024 7 $ahttp://doi.org/10.1111/mve.12664$2DOI
100 1 $aSANTOS, I. S.
245 $aAutomatic method based on deep learning to identify and account Rhipicephalus microplus larval hatching.$h[electronic resource]
260 $c2023
520 $aReports of Rhipicephalus microplus resistant populations worldwide have increased extensively, making it difficult to control this ectoparasite. The adult immersion test, commonly used to screen for acaricide resistance, produces the results only after 40 days of the tick collection because it needs the eggs to be laid and larvae to hatch. The present study aims to develop an automatic method, based on deep learning, to predict the hatching of R. microplus larva based on egg morphology. Initially, the time course of embryonic development of tick eggs was performed to discriminate between viable and non-viable eggs. Secondly, using artificial intelligence deep learning techniques, a method was developed to classify and count the eggs. The larval hatching rate of three populations of R. microplus was evaluated for the software validation process. Groups of three and six images of eggs with 12 days of embryonic development were submitted to the software to predict the larval hatching percent automatically. The results obtained by the software were compared with the prediction results of the hatching percentage performed manually by the specialist and with the results of the hatching percentage of larvae obtained in the biological assay. The group with three images of each population submitted to the software for automatic prediction of the larval hatching percent presented mean values of 96.35% ± 3.33 (Piracanjuba population), 95.98% ± 3.5 (Desterro population) and 0.0% ± 0.0 (Barbalha population). For groups with six images, the values were 94.41% ± 3.84 (Piracanjuba population), 95.93% ± 2.36 (Desterro population) and 0.0% ± 0.0 (Barbalha population). Biological assays showed the following hatching percentage values: 98% ± 1.73 (Piracanjuba population); 96% ± 2.1 (Desterro population); and 0.14% ± 0.25 (Barbalha population). 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.
650 $aCarrapato
650 $aLarva
650 $aOvo
650 $aResistência
653 $aControle
653 $aEclosão larval
653 $aLarval hatching
700 1 $aTAVARES, C. P.
700 1 $aKLAFKE, G. M.
700 1 $aRECK, J.
700 1 $aMONTEIRO, C. M. O.
700 1 $aPRATA, M. C. de A.
700 1 $aGOLO. P. S.
700 1 $aSILVA, A. C.
700 1 $aCOSTA-JUNIOR, L. M.
773 $tMedical and Veterinary Entomology$gv. 37, p. 665-674, 2023.
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