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| Registros recuperados : 757 | |
| Registros recuperados : 757 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Algodão; Embrapa Amazônia Oriental; Embrapa Arroz e Feijão; Embrapa Milho e Sorgo; Embrapa Semiárido; Embrapa Soja; Embrapa Trigo; Embrapa Uva e Vinho. |
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Data corrente: |
06/08/2018 |
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Data da última atualização: |
03/10/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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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: |
IEEE Latin America Transactions, v. 16, n. 6, p. 1749-1757, June 2018. |
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Idioma: |
Inglês
Português |
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Notas: |
Na publicação: B. A. Halfeld-Vieira, R. V. Costa, K. L. Nechet, S. A. S. Oliveira. |
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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.digipathosrep.cnptia.embrapa.br/. Keywords— plant pathology, database, deep learning, image processing. |
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Palavras-Chave: |
Aprendizagem profunda; Banco de dados; Deep learning; Imagem em processamento; Patologia vegetal; Processamento de imagem. |
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Thesagro: |
Doença de Planta. |
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Thesaurus NAL: |
Databases; Image analysis; Plant diseases and disorders; Plant pathology. |
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Categoria do assunto: |
--
H Saúde e Patologia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/182246/1/16TLA6-27GarciaArnalBarbedo.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/181156/1/16TLA6-27GarciaArnalBarbedo.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/198847/1/ID44389-2018v16n6p1749IEEELatinAmericaTransaction.pdf
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Marc: |
LEADER 02472naa a2200457 a 4500
001 2094883
005 2018-10-03
008 2018 bl uuuu u00u1 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 $aNa publicação: B. A. Halfeld-Vieira, R. V. Costa, K. L. Nechet, S. A. S. Oliveira.
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.digipathosrep.cnptia.embrapa.br/. Keywords— plant pathology, database, deep learning, image processing.
650 $aDatabases
650 $aImage analysis
650 $aPlant diseases and disorders
650 $aPlant pathology
650 $aDoença de Planta
653 $aAprendizagem profunda
653 $aBanco de dados
653 $aDeep learning
653 $aImagem em processamento
653 $aPatologia vegetal
653 $aProcessamento de imagem
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 $tIEEE Latin America Transactions$gv. 16, n. 6, p. 1749-1757, June 2018.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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