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Statements

Subject Item
n2:RIV%2F00216224%3A14310%2F14%3A00074854%21RIV15-MSM-14310___
rdf:type
n15:Vysledek skos:Concept
rdfs:seeAlso
http://journals.tubitak.gov.tr/agriculture/
dcterms:description
We present a methodical paper based on ANN to discriminate morphologically very similar species, Thrips sambuci Heeger, 1854 and T. fuscipennis Haliday, 1836 (Thysanoptera: Thripinae), as an applied case for more general use. Statistical analysis of 17 characters, measured or determined for this 2 Thrips species (reared from larvae in our laboratories), including 15 quantitative morphometric variables, was performed to elucidate morphological plasticity, detect eventual outliers, and visualize differences between the studied taxa. The computational strategy applied in this study includes a set of statistical tools (factor analysis, correlation analysis, principal component analysis, and linear discriminant analysis). This complex approach has proven the existence of 2 separate species: T. fuscipennis and T. sambuci. We present a methodical paper based on ANN to discriminate morphologically very similar species, Thrips sambuci Heeger, 1854 and T. fuscipennis Haliday, 1836 (Thysanoptera: Thripinae), as an applied case for more general use. Statistical analysis of 17 characters, measured or determined for this 2 Thrips species (reared from larvae in our laboratories), including 15 quantitative morphometric variables, was performed to elucidate morphological plasticity, detect eventual outliers, and visualize differences between the studied taxa. The computational strategy applied in this study includes a set of statistical tools (factor analysis, correlation analysis, principal component analysis, and linear discriminant analysis). This complex approach has proven the existence of 2 separate species: T. fuscipennis and T. sambuci.
dcterms:title
Artificial neural networks in online semiautomated pest discriminability: an applied case with 2 Thrips species Artificial neural networks in online semiautomated pest discriminability: an applied case with 2 Thrips species
skos:prefLabel
Artificial neural networks in online semiautomated pest discriminability: an applied case with 2 Thrips species Artificial neural networks in online semiautomated pest discriminability: an applied case with 2 Thrips species
skos:notation
RIV/00216224:14310/14:00074854!RIV15-MSM-14310___
n3:aktivita
n10:S
n3:aktivity
S
n3:cisloPeriodika
1
n3:dodaniDat
n13:2015
n3:domaciTvurceVysledku
n6:9193324 n6:6955231
n3:druhVysledku
n8:J
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n11:predkladatel
n3:idSjednocenehoVysledku
4156
n3:idVysledku
RIV/00216224:14310/14:00074854
n3:jazykVysledku
n5:eng
n3:klicovaSlova
Artificial neural networks; online semiautomated pest identification; Thysanoptera
n3:klicoveSlovo
n4:Artificial%20neural%20networks n4:Thysanoptera n4:online%20semiautomated%20pest%20identification
n3:kodStatuVydavatele
TR - Turecká republika
n3:kontrolniKodProRIV
[3370BD8C2D30]
n3:nazevZdroje
Turkish Journal of Agriculture and Forestry
n3:obor
n9:EG
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
7
n3:rokUplatneniVysledku
n13:2014
n3:svazekPeriodika
38
n3:tvurceVysledku
Peña-Méndez, Eladia Maria Doričova, Martina Fedor, Peter Vaňhara, Jaromír Prokop, Pavol Havel, Josef Kucharczyk, Halina
n3:wos
000328624300013
s:issn
1300-011X
s:numberOfPages
14
n17:doi
10.3906/tar-1305-8
n19:organizacniJednotka
14310