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Registro Completo |
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Biblioteca(s): |
Embrapa Mandioca e Fruticultura; Embrapa Meio Ambiente. |
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Data corrente: |
27/09/2018 |
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Data da última atualização: |
06/11/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
BARBEDO, J. G. A.; KOENIGKAN, L. V.; HALFELD-VIEIRA, B. de A.; COSTA, R. V. da; NECHET, K. de L.; GODOY, C. V.; LOBO JUNIOR, M.; PATRÍCIO, F. R. A.; TALAMINI, V.; CHITARRA, L. G.; OLIVEIRA, S. A. S. de; ISHIDA, A. K. N.; FERNANDES, J. M. C.; SANTOS, T. T.; CAVALCANTI, F. R.; TERAO, D.; ANGELOTTI, F. |
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Afiliação: |
JAYME GARCIA ARNAL BARBEDO, CNPTIA; LUCIANO VIEIRA KOENIGKAN, CNPTIA; BERNARDO DE ALMEIDA HALFELD VIEIRA, CNPMA; RODRIGO VERAS DA COSTA, CNPMS; KATIA DE LIMA NECHET, CNPMA; CLAUDIA VIEIRA GODOY, CNPSO; MURILLO LOBO JUNIOR, CNPAF; F. R. A. PATRÍCIO, Instituto Biológico, Campinas, SP; VIVIANE TALAMINI, CPATC; LUIZ GONZAGA CHITARRA, CNPA; SAULO ALVES SANTOS DE OLIVEIRA, CNPMF; ALESSANDRA KEIKO NAKASONE ISHIDA, CPATU; JOSE MAURICIO CUNHA FERNANDES, CNPT; THIAGO TEIXEIRA SANTOS, CNPTIA; FABIO ROSSI CAVALCANTI, CNPUV; DANIEL TERAO, CNPMA; FRANCISLENE ANGELOTTI, CPATSA. |
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Título: |
Annotated plant pathology databases for image-based detection and recognition of diseases. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Revista IEEE America Latina, v. 16, n. 6, p. 1749-1757, jun. 2018. |
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Idioma: |
Inglês
Português |
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Notas: |
O título da revista foi grafado no artigo como IEEE LATIN AMERICA TRANSACTIONS, mas registro de título do periódico é REVISTA IEEE AMÉRICA LATINA. |
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Conteúdo: |
Over the last few years, considerable effort has been spent by Embrapa in the construction of a plant disease database representative enough for the development of effective methods for automatic plant disease detection and recognition. In October of 2016, this database, called PDDB, had 2326 images of 171 diseases and other disorders affecting 21 plant species. PDDB size, although considerable, is not enough to allow the use of powerful techniques such as deep learning. In order to increase its size, each image was subdivided according to certain criteria, increasing the number of images to 46,513. Both the original (PDDB) and subdivided (XDB)databases are now being made freely available for academic research purposes, thus supporting new studies and contributing to speed up the advances in the area. Both collections are expected to grow continuously in order to expand their reach. PDDB and XDB can be accessed in the link https://www.digipathos-rep.cnptia.embrapa.br/. |
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Palavras-Chave: |
Aprendizagem profunda; Banco de dados; Imagem em processamento; Patologia vegetal. |
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Thesagro: |
Doença de Planta. |
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Thesaurus Nal: |
Databases; Plant diseases and disorders; Plant pathology. |
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Categoria do assunto: |
H Saúde e Patologia |
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Marc: |
LEADER 02382naa a2200421 a 4500
001 2097182
005 2018-11-06
008 2018 bl --- 0-- u #d
100 1 $aBARBEDO, J. G. A.
245 $aAnnotated plant pathology databases for image-based detection and recognition of diseases.$h[electronic resource]
260 $c2018
500 $aO título da revista foi grafado no artigo como IEEE LATIN AMERICA TRANSACTIONS, mas registro de título do periódico é REVISTA IEEE AMÉRICA LATINA.
520 $aOver the last few years, considerable effort has been spent by Embrapa in the construction of a plant disease database representative enough for the development of effective methods for automatic plant disease detection and recognition. In October of 2016, this database, called PDDB, had 2326 images of 171 diseases and other disorders affecting 21 plant species. PDDB size, although considerable, is not enough to allow the use of powerful techniques such as deep learning. In order to increase its size, each image was subdivided according to certain criteria, increasing the number of images to 46,513. Both the original (PDDB) and subdivided (XDB)databases are now being made freely available for academic research purposes, thus supporting new studies and contributing to speed up the advances in the area. Both collections are expected to grow continuously in order to expand their reach. PDDB and XDB can be accessed in the link https://www.digipathos-rep.cnptia.embrapa.br/.
650 $aDatabases
650 $aPlant diseases and disorders
650 $aPlant pathology
650 $aDoença de Planta
653 $aAprendizagem profunda
653 $aBanco de dados
653 $aImagem em processamento
653 $aPatologia vegetal
700 1 $aKOENIGKAN, L. V.
700 1 $aHALFELD-VIEIRA, B. de A.
700 1 $aCOSTA, R. V. da
700 1 $aNECHET, K. de L.
700 1 $aGODOY, C. V.
700 1 $aLOBO JUNIOR, M.
700 1 $aPATRÍCIO, F. R. A.
700 1 $aTALAMINI, V.
700 1 $aCHITARRA, L. G.
700 1 $aOLIVEIRA, S. A. S. de
700 1 $aISHIDA, A. K. N.
700 1 $aFERNANDES, J. M. C.
700 1 $aSANTOS, T. T.
700 1 $aCAVALCANTI, F. R.
700 1 $aTERAO, D.
700 1 $aANGELOTTI, F.
773 $tRevista IEEE America Latina$gv. 16, n. 6, p. 1749-1757, jun. 2018.
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Registro original: |
Embrapa Meio Ambiente (CNPMA) |
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| Registros recuperados : 96 | |
| 8. |  | BARBEDO, J. G. A. Counting clustered soybean seeds. In: INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, 12., 2012, Salvador. Proceedings... [S.l.]: Conference Publishing Services, 2012. p. 142-145. ICCSA 2012. Poster.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 96 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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