| FEDOR, Peter, Igor MALENOVSKÝ, Jaromír VAŇHARA, W. SIERKA a Josef HAVEL. Thrips (Thysanoptera) identification using artificial neural networks. Bulletin of Entomological Research. Cambridge, England: CAMBRIDGE UNIV PRESS, 2008, roč. 98, č. 4, s. 437-447. ISSN 0007-4853. |
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@article{806636,
author = {Fedor, Peter and Malenovský, Igor and Vaňhara, Jaromír and Sierka, W. and Havel, Josef},
article_location = {Cambridge, England},
article_number = {4},
keywords = {ANN; Thrips;identification},
language = {eng},
issn = {0007-4853},
journal = {Bulletin of Entomological Research},
title = {Thrips (Thysanoptera) identification using artificial neural networks},
volume = {98},
year = {2008}
}
TY - JOUR
ID - 806636
AU - Fedor, Peter - Malenovský, Igor - Vaňhara, Jaromír - Sierka, W. - Havel, Josef
PY - 2008
TI - Thrips (Thysanoptera) identification using artificial neural networks
JF - Bulletin of Entomological Research
VL - 98
IS - 4
SP - 437-447
EP - 437-447
PB - CAMBRIDGE UNIV PRESS
SN - 00074853
KW - ANN
KW - Thrips;identification
N2 - 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.
ER -
FEDOR, Peter, Igor MALENOVSKÝ, Jaromír VAŇHARA, W. SIERKA a Josef HAVEL. Thrips (Thysanoptera) identification using artificial neural networks. \textit{Bulletin of Entomological Research}. Cambridge, England: CAMBRIDGE UNIV PRESS, 2008, roč.~98, č.~4, s.~437-447. ISSN~0007-4853.
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