Detailed Information on Publication Record
2008
Insect identification using Artificial Neural Networks (ANN)
VAŇHARA, Jaromír, Peter FEDOR, Igor MALENOVSKÝ, Natália MURÁRIKOVÁ, Josef HAVEL et. al.Basic information
Original name
Insect identification using Artificial Neural Networks (ANN)
Name in Czech
Determinace hmyzu pomocí ANN
Authors
VAŇHARA, Jaromír (203 Czech Republic, guarantor, belonging to the institution), Peter FEDOR (703 Slovakia), Igor MALENOVSKÝ (203 Czech Republic, belonging to the institution), Natália MURÁRIKOVÁ (703 Slovakia, belonging to the institution) and Josef HAVEL (203 Czech Republic, belonging to the institution)
Edition
Proceedings CD incl. Abstracts and Author´s List ICE 2008, 2008
Other information
Language
English
Type of outcome
Konferenční abstrakt
Field of Study
10600 1.6 Biological sciences
Country of publisher
South Africa
Confidentiality degree
není předmětem státního či obchodního tajemství
RIV identification code
RIV/00216224:14310/08:00042029
Organization unit
Faculty of Science
Keywords in English
Artificial Neural Networks;ANN;Artificial Intelligence;Entomology;Identification
Tags
International impact
Změněno: 19/3/2013 15:33, prof. RNDr. Jaromír Vaňhara, CSc.
V originále
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.
In Czech
Introduction: Entomology, as well as its application in many fields, such as agriculture,forestry, human and veterinary medicine, relies heavily on the accurate identification of species. Besides molecular diagnostic techniques, the progress in information technology has opened opportunities for the computer-assisted taxonomy. Artificial Neural Networks (ANN) seem to have been one of the most promising tools for the basis of such systems. The advantages of ANN include an ability to learn from examples and to generalize observed patterns. Methods: The use of ANN requires a training database in which specimens, correctly identified by experts, are included. Each specimen has to be characterized by diagnostic variables (characters). 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 on thrips and diptera used morphometric data mostly. The high percentage of correctly identified specimens (about 97 %) is promising for a wider use of ANN for insect identification in practice. 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. High reliability of classification is promising for a wider application of ANN in the practice of insect identification.
Links
| GD524/05/H536, research and development project |
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| MSM0021622416, plan (intention) |
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