title: Thrips (Thysanoptera) identification using artificial neural networks.
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.
authors.affiliation: Comenius University, Faculty of Natural Sciences, Department of Ecosozology, Mlynská dolina, Bratislava, Slovak Republic.
authors.forename: P
authors.initials: P
authors.lastname: Fedor
authors.forename: I
authors.initials: I
authors.lastname: Malenovský
authors.forename: J
authors.initials: J
authors.lastname: Vanhara
authors.forename: W
authors.initials: W
authors.lastname: Sierka
authors.forename: J
authors.initials: J
authors.lastname: Havel
mesh_terms: D000818:Animals; D002965:Classification / Q000379:methods; D016208:Databases, Factual; D005260:Female; D007313:Insecta / Q000033:anatomy & histology / Q000145:classification*; D008297:Male; D016571:Neural Networks, Computer