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@inproceedings{709470,
author = {Havel, Josef and Vaňhara, Jaromír},
address = {Sao Paolo},
booktitle = {Proceedings of the 10th International Conference on Chemometrics in Analytical Chemistry: CHEMOMETRICS IN THE TROPICS: Nature, Medicine and Industry CAC-2006},
keywords = {ANN; Insects identification; tachinids; thrips; jumping plant-lice},
language = {eng},
location = {Sao Paolo},
pages = {26-28},
publisher = {Comitee of the 10th International Conference on Chemometrics},
title = {Evaluation of the use of Artificial Neural Networks in Taxonomy: towards automating insect identification.},
year = {2006}
}
TY - JOUR
ID - 709470
AU - Havel, Josef - Vaňhara, Jaromír
PY - 2006
TI - Evaluation of the use of Artificial Neural Networks in Taxonomy: towards automating insect identification.
PB - Comitee of the 10th International Conference on Chemometrics
CY - Sao Paolo
KW - ANN
KW - Insects identification
KW - tachinids
KW - thrips
KW - jumping plant-lice
N2 - In contrast to wide applications in chemistry, the use of ANN in taxonomy is rather rare, e.g. in chemotaxonomic identification of limpets or bioacoustics identification of Orthoptera, even if visionary study was published already in 1997 by Weeks 3. Perhaps the first real entomological application was used in the family Psychodidae (Diptera). Recently, we are building-up ANN methodology for insect identification. Appropriate key morphological characters (input) for species and utilized specimens correctly classified are creating database. With ANN we are finding model between input and species (output). In contradiction to manual identification, all characters are simultaneously taken into account over the complete database. ANN approach was developed, tested and applied in various species from three different insect orders: Diptera (Tachinidae), Thysanoptera (Thripidae) and Hemiptera (Psylloidea). Concluding, methodology developed is quite general and can be used for all entomological objects where sufficient number of characters is available and create the appropriate database After ANN “learning” the identification is fast and reliable. The approach is non-destructive unlike e.g. molecular analyses.
ER -
HAVEL, Josef a Jaromír VAŇHARA. Evaluation of the use of Artificial Neural Networks in Taxonomy: towards automating insect identification. In \textit{Proceedings of the 10th International Conference on Chemometrics in Analytical Chemistry: CHEMOMETRICS IN THE TROPICS: Nature, Medicine and Industry CAC-2006}. Sao Paolo: Comitee of the 10th International Conference on Chemometrics, 2006, s.~26-28, 2 s.
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