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

• Autor(a) principal: BARBEDO, J. G. A.
• Data de Publicação: 2018
• Outros Autores: 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.
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
• Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1097219
• topic: Patologia vegetal; Banco de dados; Aprendizagem profunda; Imagem em processamento; Doença de Planta; Plant pathology; Plant diseases and disorders; Databases

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/.