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A test of a pattern recognition system for identification of spiders

Published online by Cambridge University Press:  09 March 2007

M.T. Do ,
J.M. Harp  and
K.C. Norris
M.T. Do
Affiliation:
Department of Computer Sciences, University of Tennessee, and Oak Ridge National Laboratory, Life Sciences Division
J.M. Harp
Affiliation:
Graduate School of Biomedical Sciences, University of Tennessee/Oak Ridge National Laboratory
K.C. Norris*
Affiliation:
Department of Ecology and Evolutionary Biology, 569 Dabney Hall, University of Tennessee, Knoxville TN 37996-1610, USA
*
* Fax: (423) 974 3067 E-mail: kimnorris@utk.edu
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Abstract

Growing interest in biodiversity and conservation has increased the demand for accurate and consistent identification of arthropods. Unfortunately, professional taxonomists are already overburdened and underfunded and their numbers are not increasing with significant speed to meet the demand. In an effort to bridge the gap between professional taxonomists and non-specialists by making the results of taxonomic research more accessible, we present a partially automated pattern recognition system utilizing artificial neural networks (ANNs). Various artificial neural networks were trained to identify spider species using only digital images of female genitalia, from which key shape information had been extracted by wavelet transform. Three different sized networks were evaluated based on their ability to discriminate a test set of six species to either the genus or the species level. The species represented three genera of the wolf spiders (Araneae: Lycosidae). The largest network achieved the highest accuracy, identifying specimens to the correct genus 100% of the time and to the correct species an average of 81% of the time. In addition, the networks were most accurate when identifying specimens in a hierarchical system, first to genus and then to species. This test system was surprisingly accurate considering the small size of our training set.

Type
Review Article
Information
Bulletin of Entomological Research , Volume 89 , Issue 3 , March 1999 , pp. 217 - 224
Copyright
Copyright © Cambridge University Press 1999

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