automatic identification of species with neural networks

Clicks: 321
ID: 177781
2014
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Popular

Ranked #240 of 581 articles by views in pediatrics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 581 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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.
Reference Key
hernndez-serna2014peerjautomatic Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Andrés Hernández-Serna;Luz Fernanda Jiménez-Segura
Journal pediatrics
Year 2014
DOI
10.7717/peerj.563
URL
https://peerj.com/articles/563/
Keywords
Neural network fish plant feature extraction digital imagemedicine

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.