Bulletin of Entomological Research (2008) 98, 437-447

P. Fedor, I. Malenovský, J. Vanhara, W. Sierka and J. Havel (2008)
Thrips (Thysanoptera) identification using artificial neural networks
Bulletin of Entomological Research 98 (5), 437-447
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.
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Database assignments for author(s): Peter Fedor

Research topic(s) for pests/diseases/weeds:
identification/taxonomy
Research topic(s) for beneficials or antagonists:
identification/taxonomy

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Pest and/or beneficial records:

Beneficial	Pest/Disease/Weed	Crop/Product	Country	Quarant.
	Limothrips (genus)
	Chirothrips (genus)
	Dendrothrips (genus)
Aeolothrips (genus - predators)