Artificial neural networks in online semiautomated pest discriminability: an applied case with 2 Thrips species

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F14%3A00074854" target="_blank" >RIV/00216224:14310/14:00074854 - isvavai.cz</a>
Result on the web
<a href="http://journals.tubitak.gov.tr/agriculture/" target="_blank" >http://journals.tubitak.gov.tr/agriculture/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3906/tar-1305-8" target="_blank" >10.3906/tar-1305-8</a>

Result language
angličtina
Original language name
Artificial neural networks in online semiautomated pest discriminability: an applied case with 2 Thrips species
Original language 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.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
EG - Zoology
OECD FORD branch
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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