Publication details
Artificial neural networks in online semiautomated pest discriminability: an applied case with 2 Thrips species
| Authors | |
|---|---|
| Year of publication | 2014 |
| Type | Article in Periodical |
| Magazine / Source | Turkish Journal of Agriculture and Forestry |
| MU Faculty or unit | |
| Citation | |
| web | http://journals.tubitak.gov.tr/agriculture/ |
| Doi | http://dx.doi.org/10.3906/tar-1305-8 |
| Field | Zoology |
| Keywords | Artificial neural networks; online semiautomated pest identification; Thysanoptera |
| 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. |