Automatic identification of species with neural networks

PeerJ. 2014 Nov 4:2:e563. doi: 10.7717/peerj.563. eCollection 2014.

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.

Keywords: Butterflies; Digital image; Feature extraction; Fish; Neural network; Plant; Species.

Grants and funding

Funds for this research were provided by the Universidad de Antioquia (P2010-0010 and P2011-0015). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.