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Thrips (Thysanoptera) identification using artificial neural networks

Published online by Cambridge University Press:  21 April 2008

P. Fedor ,
I. Malenovský ,
J. Vaňhara ,
W. Sierka  and
J. Havel
P. Fedor
Affiliation:
Comenius University, Faculty of Natural Sciences, Department of Ecosozology, Mlynská dolina, 842 15 Bratislava, Slovak Republic
I. Malenovský
Affiliation:
Moravian Museum, Department of Entomology, Hviezdoslavova 29a, 627 00 Brno, Czech Republic
J. Vaňhara*
Affiliation:
Masaryk University, Faculty of Science, Department of Botany and Zoology, Kotlářská 2, 611 37 Brno, Czech Republic
W. Sierka
Affiliation:
University of Silesia, Faculty of Biology and Environmental Protection, Department of Zoology, Bankowa 9, 400 07 Katowice, Poland
J. Havel
Affiliation:
Masaryk University, Faculty of Science, Department of Chemistry, Kotlářská 2, 611 37 Brno, Czech Republic
*
*Author for correspondence E-mail: vanhara@sci.muni.cz
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Abstract

We studied the use of a supervised artificial neural network (ANN) model for semi-automated identification of 18 common European species of Thysanoptera from four genera: Aeolothrips Haliday (Aeolothripidae), Chirothrips Haliday, Dendrothrips Uzel, and Limothrips Haliday (all Thripidae). As input data, we entered 17 continuous morphometric and two qualitative two-state characters measured or determined on different parts of the thrips body (head, pronotum, forewing and ovipositor) and the sex. Our experimental data set included 498 thrips specimens. A relatively simple ANN architecture (multilayer perceptrons with a single hidden layer) enabled a 97% correct simultaneous identification of both males and females of all the 18 species in an independent test. This high reliability of classification is promising for a wider application of ANN in the practice of Thysanoptera identification.

Keywords

artificial neural networksroutine identificationThysanopteraAeolothripsChirothripsDendrothripsLimothrips
Type
Research Paper
Information
Bulletin of Entomological Research , Volume 98 , Issue 5 , October 2008 , pp. 437 - 447
Copyright
Copyright © 2008 Cambridge University Press

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