Annotated plant pathology databases for image-based detection and recognition of diseases.

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dc.contributorJAYME 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.
dc.creatorBARBEDO, J. G. A.
dc.creatorKOENIGKAN, L. V.
dc.creatorHALFELD-VIEIRA, B. de A.
dc.creatorCOSTA, R. V. da
dc.creatorNECHET, K. de L.
dc.creatorGODOY, C. V.
dc.creatorLOBO JUNIOR, M.
dc.creatorPATRÍCIO, F. R. A.
dc.creatorTALAMINI, V.
dc.creatorCHITARRA, L. G.
dc.creatorOLIVEIRA, S. A. S. de
dc.creatorISHIDA, A. K. N.
dc.creatorFERNANDES, J. M. C.
dc.creatorSANTOS, T. T.
dc.creatorCAVALCANTI, F. R.
dc.creatorTERAO, D.
dc.creatorANGELOTTI, F.
dc.date2018-09-01T00:46:41Z
dc.date2018-09-01T00:46:41Z
dc.date2018-08-31
dc.date2018
dc.date2018-10-03T11:11:11Z
dc.date.accessioned2026-07-07T04:15:37Z
dc.descriptionOver 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.
dc.descriptionNa publicação: B. A. Halfeld-Vieira, R. V. Costa, K. L. Nechet, S. A. S. Oliveira.
dc.identifierIEEE Latin America Transactions, v. 16, n. 6, p. 1749-1757, June 2018.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1094883
dc.identifierhttps://doi.org/10.1109/TLA.2018.8444395
dc.identifier.urihttp://hdl.handle.net/123456789/455809
dc.languageeng
dc.rightsopenAccess
dc.subjectPatologia vegetal
dc.subjectBanco de dados
dc.subjectAprendizagem profunda
dc.subjectProcessamento de imagem
dc.subjectDeep learning
dc.subjectDoença de Planta
dc.subjectPlant pathology
dc.subjectPlant diseases and disorders
dc.subjectDatabases
dc.subjectImage analysis
dc.titleAnnotated plant pathology databases for image-based detection and recognition of diseases.
dc.typeArtigo de periódico

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