Insect identification using Artificial Neural Networks (ANN)

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

Základní údaje

• Originální název: Insect identification using Artificial Neural Networks (ANN)
• Název česky: Determinace hmyzu pomocí ANN
• Autoři: VAŇHARA, Jaromír (203 Česká republika, garant, domácí), Peter FEDOR (703 Slovensko), Igor MALENOVSKÝ (203 Česká republika, domácí), Natália MURÁRIKOVÁ (703 Slovensko, domácí) a Josef HAVEL (203 Česká republika, domácí).
• Vydání: Proceedings CD incl. Abstracts and Author´s List ICE 2008, 2008.

Další údaje

• Originální jazyk: angličtina
• Typ výsledku: Konferenční abstrakt
• Obor: 10600 1.6 Biological sciences
• Stát vydavatele: Jižní Afrika
• Utajení: není předmětem státního či obchodního tajemství
• Kód RIV: RIV/00216224:14310/08:00042029
• Organizační jednotka: Přírodovědecká fakulta
• Klíčová slova anglicky: Artificial Neural Networks;ANN;Artificial Intelligence;Entomology;Identification
• Štítky: ANN, Artificial Intelligence, artificial neural networks, Entomology, identification
• Příznaky: Mezinárodní význam

Anotace

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.

Anotace česky

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.

Návaznosti

• GD524/05/H536, projekt VaV: Název: Evolučně ekologická analýza biologických systémů: výzkumné centrum DSP

Investor: Grantová agentura ČR, Evolučně ekologická analýza biologických systémů: výzkumné centrum DSP

• 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