| VAŇHARA, Jaromír, Natália MURÁRIKOVÁ, Igor MALENOVSKÝ and Josef HAVEL. Artificial neural networks for fly identification: A case study from the genera Tachina and Ectophasia (Diptera, Tachinidae). Biologia. Bratislava: Versita, 2007, vol. 62, No 4, p. 462—469. ISSN 1335-6372. |
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@article{752673,
author = {Vaňhara, Jaromír and Muráriková, Natália and Malenovský, Igor and Havel, Josef},
article_location = {Bratislava},
article_number = {4},
keywords = {artificial neural networks; species identification; Diptera; Tachinidae; Tachina; Ectophasia; parasitoids},
language = {eng},
issn = {1335-6372},
journal = {Biologia},
title = {Artificial neural networks for fly identification: A case study from the genera Tachina and Ectophasia (Diptera, Tachinidae)},
volume = {62},
year = {2007}
}
TY - JOUR
ID - 752673
AU - Vaňhara, Jaromír - Muráriková, Natália - Malenovský, Igor - Havel, Josef
PY - 2007
TI - Artificial neural networks for fly identification: A case study from the genera Tachina and Ectophasia (Diptera, Tachinidae)
JF - Biologia
VL - 62
IS - 4
SP - 462—469
EP - 462—469
PB - Versita
SN - 13356372
KW - artificial neural networks
KW - species identification
KW - Diptera
KW - Tachinidae
KW - Tachina
KW - Ectophasia
KW - parasitoids
N2 - The classification methodology based on morphometric data and supervised artificial neural networks (ANN) was tested on five fly species of the parasitoid genera Tachina and Ectophasia (Diptera, Tachinidae). Objects were initially photographed, then digitalized; consequently the picture was scaled and measured by means of an image analyser. The 16 variables used for classification included length of different wing veins or their parts and width of antennal segments. The sex was found to have some influence on the data and was included in the study as another input variable. Better and reliable classification was obtained when data from both the right and left wings were entered, the data from one wing were however found to be sufficient. The prediction success (correct identification of unknown test samples) varied from 88 to 100% throughout the study depending especially on the number of specimens in the training set. Classification of the studied Diptera species using ANN is possible assuming a sufficiently high number (tens) of specimens of each species is available for the ANN training. The methodology proposed is quite general and can be applied for all biological objects where it is possible to define adequate diagnostic characters and create the appropriate database.
ER -
VAŇHARA, Jaromír, Natália MURÁRIKOVÁ, Igor MALENOVSKÝ and Josef HAVEL. Artificial neural networks for fly identification: A case study from the genera Tachina and Ectophasia (Diptera, Tachinidae). \textit{Biologia}. Bratislava: Versita, 2007, vol.~62, No~4, p.~462—469. ISSN~1335-6372.
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