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Annotated Plant Pathology Databases for Image-Based Detection and Recognition of Diseases
- 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/.
- Subjects :
- 0106 biological sciences
General Computer Science
Database
Computer science
business.industry
Deep learning
Image processing
02 engineering and technology
computer.software_genre
01 natural sciences
Plant disease
0202 electrical engineering, electronic engineering, information engineering
Plant species
020201 artificial intelligence & image processing
Artificial intelligence
Electrical and Electronic Engineering
business
computer
Image based
010606 plant biology & botany
Subjects
Details
- ISSN :
- 15480992
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- IEEE Latin America Transactions
- Accession number :
- edsair.doi...........dd77994d15b3e30d6247f6d158c5b4fc