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Statements

Subject Item
n2:RIV%2F60460709%3A41340%2F13%3A60393%21RIV14-MSM-41340___
rdf:type
skos:Concept n16:Vysledek
dcterms:description
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 selected va 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 selected va
dcterms:title
Discrimination of grapevine varieties cultivated in the Czech Republic by Artificial Neural Networks Discrimination of grapevine varieties cultivated in the Czech Republic by Artificial Neural Networks
skos:prefLabel
Discrimination of grapevine varieties cultivated in the Czech Republic by Artificial Neural Networks Discrimination of grapevine varieties cultivated in the Czech Republic by Artificial Neural Networks
skos:notation
RIV/60460709:41340/13:60393!RIV14-MSM-41340___
n16:predkladatel
n17:orjk%3A41340
n3:aktivita
n18:S
n3:aktivity
S
n3:cisloPeriodika
3-4
n3:dodaniDat
n6:2014
n3:domaciTvurceVysledku
n4:1142917 n4:5154553
n3:druhVysledku
n13:J
n3:duvernostUdaju
n7:S
n3:entitaPredkladatele
n5:predkladatel
n3:idSjednocenehoVysledku
69852
n3:idVysledku
RIV/60460709:41340/13:60393
n3:jazykVysledku
n14:eng
n3:klicovaSlova
ampelography, phyllometry, Vitis vinifera, variety identification
n3:klicoveSlovo
n11:phyllometry n11:Vitis%20vinifera n11:variety%20identification n11:ampelography
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[22C7AFA1120D]
n3:nazevZdroje
Advances in Horticultural Science
n3:obor
n12:GE
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
4
n3:rokUplatneniVysledku
n6:2013
n3:svazekPeriodika
26
n3:tvurceVysledku
Mancuso, Stefano Pandolfi, Camilla Svobodová, Eva Hlásná Čepková, Petra
n3:wos
0
s:issn
0394-6169
s:numberOfPages
6
n15:organizacniJednotka
41340