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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. |
Data corrente: |
06/08/2018 |
Data da última atualização: |
03/10/2018 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
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. |
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. |
Título: |
Annotated plant pathology databases for image-based detection and recognition of diseases. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
IEEE Latin America Transactions, v. 16, n. 6, p. 1749-1757, June 2018. |
Idioma: |
Inglês Português |
Notas: |
Na publicação: B. A. Halfeld-Vieira, R. V. Costa, K. L. Nechet, S. A. S. Oliveira. |
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. |
Palavras-Chave: |
Aprendizagem profunda; Banco de dados; Deep learning; Imagem em processamento; Patologia vegetal; Processamento de imagem. |
Thesagro: |
Doença de Planta. |
Thesaurus Nal: |
Databases; Image analysis; Plant diseases and disorders; Plant pathology. |
Categoria do assunto: |
-- H Saúde e Patologia |
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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Embrapa Agricultura Digital (CNPTIA) |
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Registro Completo
Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
Data corrente: |
30/10/2019 |
Data da última atualização: |
30/10/2019 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
B - 1 |
Autoria: |
MATOS, A. P. de. |
Afiliação: |
ARISTOTELES PIRES DE MATOS, CNPMF. |
Título: |
Main pests affecting pineapple plantations and their impact on crop development. |
Ano de publicação: |
2019 |
Fonte/Imprenta: |
Acta Horticulturae, n. 1239, 2019. |
DOI: |
10.17660/ActaHortic.2019.1239.17 |
Idioma: |
Inglês |
Notas: |
Proc. IX International Pineapple Symposium |
Conteúdo: |
Pineapple is part of the Bromeliaceae botanical family. It is native to South America and was disseminated throughout Central America and the Caribbean, probably by fruit trading for consumption among the native people. Portuguese and Spanish people took the pineapple to several countries during the 16th century. Nowadays pineapple is grown commercially in about 80 countries in the tropics and in some warm subtropical regions. In many of these countries some pests, diseases and weeds have been reported as main constraints for pineapple production. Significant yield losses have been caused by bacterial fruit collapse, bacterial heart rot, Phytophthora heart and root rots, fruitlet core rot, fusariosis, nematodes, symphillids, mealybug wilt, mites, fruit borer, pink disease, black rot, internal browning and several weeds. Special characteristics of the pineapple crop, such as all year-round fruit production and fields with plant and the ratoon crops plants in several developmental stages, increases the incidence and permanence of those pests and diseases. This paper will focus on interactions of pests, diseases and weeds with the pineapple crop as important knowledge towards the definition of an effective integrated pest, disease and weed management program for sustainable production. |
Thesagro: |
Abacaxi. |
Categoria do assunto: |
-- |
Marc: |
LEADER 01803naa a2200157 a 4500 001 2113636 005 2019-10-30 008 2019 bl uuuu u00u1 u #d 024 7 $a10.17660/ActaHortic.2019.1239.17$2DOI 100 1 $aMATOS, A. P. de 245 $aMain pests affecting pineapple plantations and their impact on crop development.$h[electronic resource] 260 $c2019 500 $aProc. IX International Pineapple Symposium 520 $aPineapple is part of the Bromeliaceae botanical family. It is native to South America and was disseminated throughout Central America and the Caribbean, probably by fruit trading for consumption among the native people. Portuguese and Spanish people took the pineapple to several countries during the 16th century. Nowadays pineapple is grown commercially in about 80 countries in the tropics and in some warm subtropical regions. In many of these countries some pests, diseases and weeds have been reported as main constraints for pineapple production. Significant yield losses have been caused by bacterial fruit collapse, bacterial heart rot, Phytophthora heart and root rots, fruitlet core rot, fusariosis, nematodes, symphillids, mealybug wilt, mites, fruit borer, pink disease, black rot, internal browning and several weeds. Special characteristics of the pineapple crop, such as all year-round fruit production and fields with plant and the ratoon crops plants in several developmental stages, increases the incidence and permanence of those pests and diseases. This paper will focus on interactions of pests, diseases and weeds with the pineapple crop as important knowledge towards the definition of an effective integrated pest, disease and weed management program for sustainable production. 650 $aAbacaxi 773 $tActa Horticulturae$gn. 1239, 2019.
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