Insect identification using Artificial Neural Networks (ANN)
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F08%3A00042029" target="_blank" >RIV/00216224:14310/08:00042029 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Insect identification using Artificial Neural Networks (ANN)
Original language description
Introduction: The progress in information technology has opened opportunities for the computer-assisted taxonomy. Methods: The use of ANN requires a training database in which specimens, correctly identified by experts, are included. For ANN inputs can be used digital images, optically sensed wing beat frequency spectra, near-infrared reflectance spectra, bioacoustic recordings, chemotaxonomy or morphometry. An ANN model is designed to find a relationship between the characters (=input) and species (=output). The quality of the training set is an essential prerequisite to obtaining reliable identifications. Results: Our case studies used morphometric data mostly. The high percentage of correctly identified specimens (about 97 %) is promising for a wider use of ANN. Conclusions: ANN is cheap and non-destructive suitable also for type material or permanently mounted slides. ANN have the potential to enhance the practice of routine identification with a non-expert as technical help.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
EG - Zoology
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2008
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů