Evaluation of the use of Artificial Neural Networks in
Taxonomy: towards automating insect identification.

D 2006

Evaluation of the use of Artificial Neural Networks in Taxonomy: towards automating insect identification.

HAVEL, Josef a Jaromír VAŇHARA

Základní údaje

Originální název

Evaluation of the use of Artificial Neural Networks in Taxonomy: towards automating insect identification.

Název česky

Evaluace vyuziti umelych neuronovych siti v taxonomii: kroky k automatizaci urcovani hmyzu

Autoři

HAVEL, Josef (203 Česká republika) a Jaromír VAŇHARA (203 Česká republika, garant)

Vydání

Sao Paolo, Proceedings of the 10th International Conference on Chemometrics in Analytical Chemistry: CHEMOMETRICS IN THE TROPICS: Nature, Medicine and Industry CAC-2006, od s. 26-28, 2 s. 2006

Nakladatel

Comitee of the 10th International Conference on Chemometrics

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10600 1.6 Biological sciences

Stát vydavatele

Brazílie

Utajení

není předmětem státního či obchodního tajemství

Kód RIV

RIV/00216224:14310/06:00018395

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

ANN; Insects identification; tachinids; thrips; jumping plant-lice

Štítky

ANN, Insects identification, jumping plant-lice, tachinids, thrips

Příznaky

Mezinárodní význam

Změněno: 24. 1. 2007 13:50, prof. RNDr. Jaromír Vaňhara, CSc.

Anotace

ORIG CZ

V originále

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.

Česky

Při srovnání využití ANN v chemii a dalších oborech je využití v taxonomii velmi malé, např. identifikace Patella or bioakustická identificae Orthoptera, přestože vizionářská studie pochází již z 1997. V současné době budujeme ANN metodologii pro klasifikaci hmyzu.

Návaznosti

MSM0021622416, záměr

Název: Diverzita biotických společenstev a populací: kauzální analýza variability v prostoru a čase

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Diverzita biotických společenstev: kauzální analýza variability v prostoru a čase

Citovat

HAVEL, Josef a Jaromír VAŇHARA. Evaluation of the use of Artificial Neural Networks in Taxonomy: towards automating insect identification. In 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.

@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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