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Automating the identification of insects: a new solution to an old problem

Published online by Cambridge University Press:  10 July 2009

P.J.D. Weeks ,
I.D. Gauld ,
K.J. Gaston  and
M.A. O'Neill
P.J.D. Weeks
Affiliation:
Department of Entomology, The Natural History Museum, Cromwell Road, London, SW7 5BD, UK:
I.D. Gauld*
Affiliation:
Department of Entomology, The Natural History Museum, Cromwell Road, London, SW7 5BD, UK:
K.J. Gaston
Affiliation:
Department of Animal and Plant Sciences, University of Sheffield, Sheffield, S10 2TN, UK:
M.A. O'Neill
Affiliation:
Oxford Orthopaedic Engineering Centre, Nuffield NHS Trust, Windmill Road, Oxford, OX3 7LD, UK
*
* Author for correspondence.
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Abstract

In this paper we describe a semi-automated digital image analysis system which is capable of discriminating five closely related species of Ichneumonidae. Specimens were distinguished by differences in their wings. The system functions by (a) extracting the significant variation (principal components) among a training set of images of the same species, (b) using these principal components to efficiently represent the morphology of wings of that species, and (c) exploiting the fact that images of the same species will share characteristic principal components, while images of different species will not. Such an approach allows the construction of modular species classifiers, to which like species correlate strongly, while dissimilar species do not. A recognition accuracy of 94% was achieved when the system was tested on 175 images of wings of the five ichneumonids. The wing images were caricatured to accentuate their venation and pigmentation patterns.

Type
Original Articles
Information
Bulletin of Entomological Research , Volume 87 , Issue 2 , April 1997 , pp. 203 - 211
Copyright
Copyright © Cambridge University Press 1997

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References

Alberch, P. (1993) Museums, collections and biodiversity inventories. Trends in Ecology and Evolution 8, 372–375.CrossRefGoogle ScholarPubMed
Convention on Biological Diversity (1992) Text and annexes. 34 pp. Geneva, Interim Secretariat for the CBD.Google Scholar
Daly, H.V. & Balling, S.S. (1978) Identification of Africanized honey bees in the Western Hemisphere by discriminant analysis. Journal of the Kansas Entomological Society 51, 857–869.Google Scholar
Daly, H.V., Hoelmer, K., Norman, P. & Allen, T. (1982) Computer-assisted measurement and identification of honey bees (Hymenoptera: Apidae). Annals of the Entomological Society of America 75, 591–594.CrossRefGoogle Scholar
Dupraw, E.J. (1965) The recognition and handling of honey-bee specimens in non-Linnean taxonomy. Journal of Apicultural Research 4, 71–84.CrossRefGoogle Scholar
Gaston, K.J. & May, R.M. (1992) Taxonomy of taxonomists. Nature 356, 281–282.CrossRefGoogle Scholar
Gauld, I.D. (1986) Taxonomy, its limitations and its role in understanding parasitoid biology, pp. 1–21in Waage, J. & Greathead, D. (Eds) Insect parasitoids. London, Academic Press.Google Scholar
Gauld, I.D. (1991) The Ichneumonidae of Costa Rica, 1. Memoirs of the American Entomological Institute 47, 1–589.Google Scholar
Holden, C. (1989) Entomologists wane as insects wax. Science 246, 754–756.CrossRefGoogle ScholarPubMed
House of Lords Select Committee on Science and Technology (1992) Systematic biology research. HL paper 22-I. 106 pp. London, HMSO.Google Scholar
Pankhurst, R.J. (1978) Biological identification. 104 pp. London,Arnold.Google Scholar
Pentland, A., Moghaddam, B. & Starner, T. (1994) View-based and modular eigenspaces for face recognition, pp. 84–91 in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,Seattle, Washington.CrossRefGoogle Scholar
Press, W.H., Teukolsky, S.A., Vetterling, W.T. & Flannery, B.P. (1994) Statistical description of data. pp. 609–655 in Numerical Recipes in C.Cambridge, Cambridge University Press.Google Scholar
Rinderer, T.E., Buco, S.M., Rubink, W.L., Daly, H.V., Stelzer, J.A., Riggio, R.M. & Baptista, F.C. (1993) Morphometric identification of Africanized and European honey bees using large reference populations Apidologie 24, 569–585.CrossRefGoogle Scholar
Russ, J.C. (1995) The image processing handbook. 2nd edn.674 pp. Boca Raton, CRC Press.Google Scholar
Tilling, S.M. (1987) Education and taxonomy: the role of the Field Studies Council and AIDGAP. pp. 87–96in Berry, R.J. & Crothers, J.H. (Eds) Nature, natural history and ecology. London, Academic Press.Google Scholar
Turk, M. & Pentland, A. (1991) Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86.CrossRefGoogle ScholarPubMed
Valentin, D., Abdi, H., O'Toole, A.J. & Cottrell, G.W. (1994) Connectionist models of face processing: a survey. Pattern Recognition 27, 1209–1230.CrossRefGoogle Scholar
Weeks, P.J.D. & Gaston, K.J. (in press) Image analysis, neural networks, and the taxonomic impediment to biodiversity studies. Biodiversity and Conservation.Google Scholar
White, I.M. & Scott, P.R. (1994) Computer information resources for pest identification: A review. pp. 129–137in Hawksworth, D.L. (Ed.) The identification and characterisation of pest organisms. Wallingford, CAB International.Google Scholar
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, 389–395.CrossRefGoogle Scholar