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Registro Completo |
Biblioteca(s): |
Embrapa Amapá; Embrapa Amazônia Oriental; Embrapa Meio-Norte; Embrapa Unidades Centrais. |
Data corrente: |
03/11/1997 |
Data da última atualização: |
06/10/2022 |
Tipo da produção científica: |
Folder/Folheto/Cartilha |
Autoria: |
SOUZA FILHO, A. P. da S.; NEVES, M. do P. H. das; DANTAS, M.; SERRAO, E. A. S. |
Afiliação: |
ANTONIO PEDRO DA SILVA SOUZA FILHO, CPAF-AP; MARIA DO PILAR HENRIQUES DAS NEVES, CPATU; MARIO DANTAS, CPATU; EMANUEL ADILSON DE SOUZA SERRAO, CPATU. |
Título: |
Introdução e avaliação dos gêneros Brachiaria, Cloris, Cynodon, Digitaria, Eragrostis, Panicum e Tripsacum nas condições de campos cerrados do Amapá. |
Ano de publicação: |
1982 |
Fonte/Imprenta: |
Macapá: EMBRAPA-UEPAT de Macapá, 1982. |
Páginas: |
3 p. |
Série: |
(EMBRAPA-UEPAT de Macapá. Pesquisa em andamento, 22). |
Idioma: |
Português |
Conteúdo: |
Visando selecionar germoplasma de gramíneas forrageiras de alto potencial produtivo, alto valor nutritivo, de boa capacidade de adaptação as condições de solo e a períodos relativamente longos de estiagem, foram introduzidos, nos solos sob vegetação de campos cerrados do Amapá, diversos ecotipos pertencentes aos generos Brachiaria, Cloris, Cynodon, Digitaria, Eragrostis, Panicum e Tripsacum. |
Palavras-Chave: |
Amapa; Brasil. |
Thesagro: |
Brachiaria; Cerrado; Germoplasma; Gramínea Forrageira; Seleção. |
Thesaurus Nal: |
Brazil; forage; germplasm; grasses; savannas. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/65003/1/AP-1982-introducao-avaliacao-generos-brachiaria.pdf
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Marc: |
LEADER 01312nam a2200313 a 4500 001 1341760 005 2022-10-06 008 1982 bl uuuu u0uu1 u #d 100 1 $aSOUZA FILHO, A. P. da S. 245 $aIntrodução e avaliação dos gêneros Brachiaria, Cloris, Cynodon, Digitaria, Eragrostis, Panicum e Tripsacum nas condições de campos cerrados do Amapá. 260 $aMacapá: EMBRAPA-UEPAT de Macapá$c1982 300 $a3 p. 490 $a(EMBRAPA-UEPAT de Macapá. Pesquisa em andamento, 22). 520 $aVisando selecionar germoplasma de gramíneas forrageiras de alto potencial produtivo, alto valor nutritivo, de boa capacidade de adaptação as condições de solo e a períodos relativamente longos de estiagem, foram introduzidos, nos solos sob vegetação de campos cerrados do Amapá, diversos ecotipos pertencentes aos generos Brachiaria, Cloris, Cynodon, Digitaria, Eragrostis, Panicum e Tripsacum. 650 $aBrazil 650 $aforage 650 $agermplasm 650 $agrasses 650 $asavannas 650 $aBrachiaria 650 $aCerrado 650 $aGermoplasma 650 $aGramínea Forrageira 650 $aSeleção 653 $aAmapa 653 $aBrasil 700 1 $aNEVES, M. do P. H. das 700 1 $aDANTAS, M. 700 1 $aSERRAO, E. A. S.
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Embrapa Amapá (CPAF-AP) |
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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
Biblioteca(s): |
Embrapa Gado de Leite. |
Data corrente: |
30/11/2023 |
Data da última atualização: |
30/11/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
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. |
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. |
Título: |
Automatic method based on deep learning to identify and account Rhipicephalus microplus larval hatching. |
Ano de publicação: |
2023 |
Fonte/Imprenta: |
Medical and Veterinary Entomology, v. 37, p. 665-674, 2023. |
DOI: |
http://doi.org/10.1111/mve.12664 |
Idioma: |
Inglês |
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 |
Palavras-Chave: |
Controle; Eclosão larval; Larval hatching. |
Thesagro: |
Carrapato; Larva; Ovo; Resistência. |
Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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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