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Automatic identification of species with neural networks
A new automatic identification system using photographic images has been designed to recognize fish, plant, and butterfly species from Europe and South America. The automatic classification system integrates multiple image processing tools to extract the geometry, morphology, and texture of the imag...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4226643/ https://www.ncbi.nlm.nih.gov/pubmed/25392749 http://dx.doi.org/10.7717/peerj.563 |
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author | Hernández-Serna, Andrés Jiménez-Segura, Luz Fernanda |
author_facet | Hernández-Serna, Andrés Jiménez-Segura, Luz Fernanda |
author_sort | Hernández-Serna, Andrés |
collection | PubMed |
description | A new automatic identification system using photographic images has been designed to recognize fish, plant, and butterfly species from Europe and South America. The automatic classification system integrates multiple image processing tools to extract the geometry, morphology, and texture of the images. Artificial neural networks (ANNs) were used as the pattern recognition method. We tested a data set that included 740 species and 11,198 individuals. Our results show that the system performed with high accuracy, reaching 91.65% of true positive fish identifications, 92.87% of plants and 93.25% of butterflies. Our results highlight how the neural networks are complementary to species identification. |
format | Online Article Text |
id | pubmed-4226643 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-42266432014-11-12 Automatic identification of species with neural networks Hernández-Serna, Andrés Jiménez-Segura, Luz Fernanda PeerJ Biodiversity A new automatic identification system using photographic images has been designed to recognize fish, plant, and butterfly species from Europe and South America. The automatic classification system integrates multiple image processing tools to extract the geometry, morphology, and texture of the images. Artificial neural networks (ANNs) were used as the pattern recognition method. We tested a data set that included 740 species and 11,198 individuals. Our results show that the system performed with high accuracy, reaching 91.65% of true positive fish identifications, 92.87% of plants and 93.25% of butterflies. Our results highlight how the neural networks are complementary to species identification. PeerJ Inc. 2014-11-04 /pmc/articles/PMC4226643/ /pubmed/25392749 http://dx.doi.org/10.7717/peerj.563 Text en © 2014 Hernández-Serna and Jimenéz-Segura http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Biodiversity Hernández-Serna, Andrés Jiménez-Segura, Luz Fernanda Automatic identification of species with neural networks |
title | Automatic identification of species with neural networks |
title_full | Automatic identification of species with neural networks |
title_fullStr | Automatic identification of species with neural networks |
title_full_unstemmed | Automatic identification of species with neural networks |
title_short | Automatic identification of species with neural networks |
title_sort | automatic identification of species with neural networks |
topic | Biodiversity |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4226643/ https://www.ncbi.nlm.nih.gov/pubmed/25392749 http://dx.doi.org/10.7717/peerj.563 |
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