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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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References

Boddy, L., Morris, C.W. & Wimpenny, J.W.T. (1990) Introduction to neural networks. Binary 2, 179–185.Google Scholar
Boddy, L., Morris, C.W., Wilkins, M.F., Tarran, G.A. & Burkill, P.H. (1994) Neural network analysis of flow cytometric data for 40 marine phytoplankton species. Cytometry 15, 283–293.CrossRefGoogle ScholarPubMed
Bungay, H. & Bungay, M.L. (1991) Identifying microorganisms with a neural network. Binary 3, 51–52.Google Scholar
Davis, G.M. (1995) Systematics and public health. BioScience 45, 705–714.CrossRefGoogle Scholar
Dondale, C.D. & Redner, J.H. (1990) The wolf spiders, nurseryweb spiders and lynx spiders of Canada and Alaska. Insects and arachnids of Canada part 17. Ottawa, Agricultural Canada.Google Scholar
Edwards, M. & Morse, D.R. (1995) The potential for computer-aided identification in biodiversity research. Trends in Ecology and Evolution 10, 153–158.CrossRefGoogle ScholarPubMed
Fahlman, S.E. (1988) An empirical study of learning speed in back propagation network. Technical Report. CMU-CS-88-162, Carnegie Mellon University, Pittsburgh, Pennsylvania.Google Scholar
Fahlman, S.E. & Lebiere, C. (1991) The cascade-correlation learning architecture. Technical Report. CMU-CS-90-106. Carnegie Mellon University, Pittsburgh, Pennsylvania.Google Scholar
Graps, A. (1995) An introduction to wavelets. The Institute of Electrical and Electronics Engineers (IEEE) Computational Science and Engineering 2, 50–61.Google Scholar
McCormack, D.K.R. (1994) An investigation into the representation of data for the neural implementation of a handwritten static signature verification system. PhD thesis, University of Wales, College of Cardiff.Google Scholar
Miller, D.R. & Rossman, A.Y. (1995) Systematics, biodiversity, and agriculture. BioScience 45, 680–696.CrossRefGoogle Scholar
Moallemi, C. (1991) Classifying cells for cancer diagnosis using neural networks. IEEE Expert 6, 8–12.CrossRefGoogle Scholar
NOVA (1975) ‘Take the World from Another Point of View’, episode origially broadcast 2 February 1975, PBS.Google Scholar
Rataj, T. & Schindler, J. (1991) Identification of bacteria by multi layer neural networks. Binary 3, 159–164.Google Scholar
Simpson, R., Williams, R., Ellis, R. & Culverhouse, P.F. (1992) Biological pattern recognition by neural networks. Marine Ecology Progress Series 79, 303–308.CrossRefGoogle Scholar
Systematics Agenda 2000 (A Consortium of the American Society of Plant Taxonomists, the Society of Systematic Biologists, and the Willi Hennig Society, in cooperation with the Association of Systematics Collections) (1994) Systematics Agenda 2000: Charting the biosphere. Technical Report.Google Scholar
Weeks, P.J.D. & Gaston, K.J. (1997) Image analysis, neural networks, and the taxonomic impediment to biodiversity studies. Biodiversity and Conservation 6, 263–274.CrossRefGoogle Scholar
Weeks, P.J.D., Gauld, I.D., Gaston, K.J. & O'Neill, M.A. (1997) Automating the identification of insects: a new solution to an old problem. Bulletin of Entomological Research 87, 203–211.CrossRefGoogle Scholar
Wilkins, M.F., Boddy, L., Morris, C.W. & Jonker, R. (1996) A comparison of some neural and non-neural methods for identification of phytoplankton from flow cytometry data. Cabios 12, 9–18.Google ScholarPubMed
Yu, D.S., Kokko, E.G., Barron, J.R., Schaalje, G.B. & Gowen, B.E. (1992) Identification of ichneumonid wasps using image analysis of wings. Systematic Entomology 17, 389–95.CrossRefGoogle Scholar