a1 Comenius University, Faculty of Natural Sciences, Department of Ecosozology, Mlynská dolina, 842 15 Bratislava, Slovak Republic
a2 Moravian Museum, Department of Entomology, Hviezdoslavova 29a, 627 00 Brno, Czech Republic
a3 Masaryk University, Faculty of Science, Department of Botany and Zoology, Kotlářská 2, 611 37 Brno, Czech Republic
a4 University of Silesia, Faculty of Biology and Environmental Protection, Department of Zoology, Bankowa 9, 400 07 Katowice, Poland
a5 Masaryk University, Faculty of Science, Department of Chemistry, Kotlářská 2, 611 37 Brno, Czech Republic
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.
(Accepted November 20 2007)
(Online publication April 21 2008)