Insect identification using Artificial Neural Networks (ANN)

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Authors	VAŇHARA Jaromír FEDOR Peter MALENOVSKÝ Igor MURÁRIKOVÁ Natália HAVEL Josef
Year of publication	2008
Type	Conference abstract
MU Faculty or unit	Faculty of Science
Citation
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.
Related projects:	Diversity of Biotic Communities and Populations: Causal Analysis of variation in space and time Evolutionary ecological analysis of biological systems: research centrum for PhD studies