Going deeper in the automated identification of Herbarium specimens
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| Autores: | , , , , |
|---|---|
| Formato: | artículo original |
| Fecha de Publicación: | 2017 |
| Descripción: | Hundreds of herbarium collections have accumulated a valuable heritage and knowledge of plants over several centuries. Recent initiatives started ambitious preservation plans to digitize this information and make it available to botanists and the general public through web portals. However, thousands of sheets are still unidentified at the species level while numerous sheets should be reviewed and updated following more recent taxonomic knowledge. These annotations and revisions require an unrealistic amount of work for botanists to carry out in a reasonable time. Computer vision and machine learning approaches applied to herbarium sheets are promising but are still not well studied compared to automated species identification from leaf scans or pictures of plants in the field. |
| País: | RepositorioTEC |
| Institución: | Instituto Tecnológico de Costa Rica |
| Repositorio: | RepositorioTEC |
| Lenguaje: | Inglés |
| OAI Identifier: | oai:repositoriotec.tec.ac.cr:2238/7326 |
| Acceso en línea: | https://doi.org/10.1186/s12862-017-1014-z https://hdl.handle.net/2238/7326 |
| Access Level: | acceso abierto |
| Palabra clave: | Informática Biodiversidad Visión artificial Botánica Identificación Aprendizaje Research Subject Categories::NATURAL SCIENCES::Biology::Organism biology::Plant physiology Research Subject Categories::TECHNOLOGY::Information technology::Image analysis |