Discrimination of grapevine varieties cultivated in the Czech Republic by Artificial Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41340%2F13%3A60393" target="_blank" >RIV/60460709:41340/13:60393 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Discrimination of grapevine varieties cultivated in the Czech Republic by Artificial Neural Networks

Popis výsledku v původním jazyce

An artificial neural network approach, based on fractal leaf parameters, and classical ampelography were used to identify nine grapevine varieties cultivated at the St. Claire?s vineyard, Prague Botanic Garden. Fifty healthy, fully-expanded leaves were collected for each variety, scanned using an optical scanner and then elaborated by computer programs. Fourteen phyllometric parameters were qualitatively and quantitatively analysed by the digital image analysis. Comparative frames were constructed for each variety and the relationships among varieties were assessed using artificial neural networks. Results were then compared with the outcome from traditional ampelographic analysis. The Artificial Neural Network technique appears to be a complementary approach to the traditional ampelography methods commonly used for cultivar discrimination, since the equipment necessary for this analysis is very inexpensive and available. Application of the technique led to the distinction of nine sele

Název v anglickém jazyce

Discrimination of grapevine varieties cultivated in the Czech Republic by Artificial Neural Networks

Popis výsledku anglicky

An artificial neural network approach, based on fractal leaf parameters, and classical ampelography were used to identify nine grapevine varieties cultivated at the St. Claire?s vineyard, Prague Botanic Garden. Fifty healthy, fully-expanded leaves were collected for each variety, scanned using an optical scanner and then elaborated by computer programs. Fourteen phyllometric parameters were qualitatively and quantitatively analysed by the digital image analysis. Comparative frames were constructed for each variety and the relationships among varieties were assessed using artificial neural networks. Results were then compared with the outcome from traditional ampelographic analysis. The Artificial Neural Network technique appears to be a complementary approach to the traditional ampelography methods commonly used for cultivar discrimination, since the equipment necessary for this analysis is very inexpensive and available. Application of the technique led to the distinction of nine sele

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

GE - Šlechtění rostlin

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Advances in Horticultural Science

ISSN

0394-6169

e-ISSN

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Svazek periodika

26

Číslo periodika v rámci svazku

3-4

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

6

Strana od-do

187-192

Kód UT WoS článku

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EID výsledku v databázi Scopus

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