Insect identification using Artificial Neural Networks (ANN)

VAŇHARA, Jaromír, Peter FEDOR, Igor MALENOVSKÝ, Natália MURÁRIKOVÁ and Josef HAVEL. Insect identification using Artificial Neural Networks (ANN). In Proceedings CD incl. Abstracts and Author´s List ICE 2008. 2008.

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

• Original language: English
• Type of outcome: Conference abstract
• Field of Study: 10600 1.6 Biological sciences
• Country of publisher: South Africa
• Confidentiality degree: is not subject to a state or trade secret
• 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: ANN, Artificial Intelligence, artificial neural networks, Entomology, identification
• Tags: International impact

Abstract

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.

Abstract (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: Name: Evolučně ekologická analýza biologických systémů: výzkumné centrum DSP

Investor: Czech Science Foundation, Evolutionary ecological analysis of biological systems: research centrum for PhD studies

• MSM0021622416, plan (intention): Name: Diverzita biotických společenstev a populací: kauzální analýza variability v prostoru a čase

Investor: Ministry of Education, Youth and Sports of the CR, Diversity of Biotic Communities and Populations: Causal Analysis of variation in space and time