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Annotated Plant Pathology Databases for Image-Based Detection and Recognition of Diseases

Authors :
Kátia de Lima Nechet
Luciano Vieira Koenigkan
Daniel Terao
Flavia Rodrigues Alves Patricio
Rodrigo Veras Costa
Fábio Rossi Cavalcanti
Saulo Alves Santos de Oliveira
José Maurício Cunha Fernandes
Jayme Garcia Arnal Barbedo
Claudia Vieira Godoy
T. T. Santos
Viviane Talamini
A. K. N. Ishida
Murillo Lobo Junior
Luiz Gonzaga Chitarra
Bernardo de Almeida Halfeld-Vieira
Francislene Angelotti
Source :
IEEE Latin America Transactions. 16:1749-1757
Publication Year :
2018
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2018.

Abstract

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/.

Details

ISSN :
15480992
Volume :
16
Database :
OpenAIRE
Journal :
IEEE Latin America Transactions
Accession number :
edsair.doi...........dd77994d15b3e30d6247f6d158c5b4fc