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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