Thrips (Thysanoptera) identification using artificial neural networks

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Authors	FEDOR Peter MALENOVSKÝ Igor VAŇHARA Jaromír SIERKA W. HAVEL Josef
Year of publication	2008
Type	Article in Periodical
Magazine / Source	Bulletin of Entomological Research
MU Faculty or unit	Faculty of Science
Citation
Field	Zoology
Keywords	ANN; Thrips;identification
Description	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.
Related projects:	Diversity of Biotic Communities and Populations: Causal Analysis of variation in space and time