English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/173350
Share/Impact:
Statistics
logo share SHARE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

Large-Scale Plant Classification with Deep Neural Networks

AuthorsHeredia, Ignacio
KeywordsBiodiversity monitoring
Citizen science
Deep learning
Plant classification
Issue Date2017
PublisherAssociation for Computing Machinery
CitationProceedings of the Computing Frontiers Conference: 259-262 (2017)
SeriesCF'17
AbstractThis paper discusses the potential of applying deep learning techniques for plant classification and its usage for citizen science in large-scale biodiversity monitoring. We show that plant classification using near state-of-the-art convolutional network architectures like ResNet50 achieves significant improvements in accuracy compared to the most widespread plant classification application in test sets composed of thousands of different species labels. We find that the predictions can be confidently used as a baseline classification in citizen science communities like iNaturalist (or its Spanish fork, Natusfera) which in turn can share their data with biodiversity portals like GBIF.
DescriptionConferencia celebrada en Siena (Italia) del 15 al 17 de mayo de 2017.
Publisher version (URL)https://doi.org/10.1145/3075564.3075590
URIhttp://hdl.handle.net/10261/173350
DOI10.1145/3075564.3075590
ISBN978-1-4503-4487-6
ReferencesHeredia, Ignacio. Theano plant classification. http://hdl.handle.net/10261/173351 .
Appears in Collections:(IFCA) Libros y partes de libros
Files in This Item:
File Description SizeFormat 
accesoRestringido.pdf59,24 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.