FEDOR, Peter, Igor MALENOVSKÝ, Jaromír VAŇHARA, W. SIERKA and Josef HAVEL. Thrips (Thysanoptera) identification using artificial neural networks. Bulletin of Entomological Research. Cambridge, England: CAMBRIDGE UNIV PRESS, 2008, vol. 98, No 4, p. 437-447. ISSN 0007-4853.
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Basic information
Original name Thrips (Thysanoptera) identification using artificial neural networks
Name in Czech Determinace třásněnek za pomoci ANN.
Authors FEDOR, Peter (703 Slovakia), Igor MALENOVSKÝ (203 Czech Republic), Jaromír VAŇHARA (203 Czech Republic, guarantor, belonging to the institution), W. SIERKA (616 Poland) and Josef HAVEL (203 Czech Republic, belonging to the institution).
Edition Bulletin of Entomological Research, Cambridge, England, CAMBRIDGE UNIV PRESS, 2008, 0007-4853.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10600 1.6 Biological sciences
Country of publisher United Kingdom of Great Britain and Northern Ireland
Confidentiality degree is not subject to a state or trade secret
Impact factor Impact factor: 1.415
RIV identification code RIV/00216224:14310/08:00027185
Organization unit Faculty of Science
UT WoS 000260173700002
Keywords in English ANN; Thrips;identification
Tags ANN, identification, thrips
Tags International impact, Reviewed
Changed by Changed by: prof. RNDr. Jaromír Vaňhara, CSc., učo 391. Changed: 19/3/2013 15:37.
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.
Abstract (in Czech)
Modelové využití supervised artificial neural network (ANN) pro identifikaci 18 evropských druhů trásněnek 4 rodů.
Links
MSM0021622416, plan (intention)Name: Diverzita biotických společenstev a populací: kauzální analýza variability v prostoru a čase
Investor: Ministry of Education, Youth and Sports of the CR, Diversity of Biotic Communities and Populations: Causal Analysis of variation in space and time
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