Yazar "Egesel, C. O." seçeneğine göre listele

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    Investigating Yield and Quality Traits of Some Bread Wheat (Triticum aestivum L.) Genotypes Based on Gliadin Band Variations using Biplot Analysis
    (Univ Namik Kemal, 2014) Baytekin, H.; Egesel, C. O.; Kahriman, F.; Aktar, M.; Tuncel, N. B.
    This study was carried out to investigate and compare 40 wheat bread genotypes with different origins for their agronomic traits, grain yield, flour quality traits, and gliadin band variations. The field trial was conducted in 20082009 and 2009-2010 growing seasons at the Dardanos Research and Application Center of canakkaie Onsekiz Mart University, in canakkaie, Turkey. A new statistical method, calibrated biplot analysis, was used to compare the genotypes for the investigated traits, based on their gliadin band variation. Gliadin band analysis resulted in 7 different genotype groups, and significant variations were detected within each group. The genotypes originated in proximate or similar regions were found to be genetically close as suggested by the gliadin band analysis. Biplot analysis detected that some genotypes had similarities in terms of agronomic and quality traits within their respective gliadin band groups, whereas it was not quite possible to make a clear distinction for all of the genotypes. Overall results suggested that Sagittario, one of the prevalent varieties of the region, could be recommended to growers as a high yielding and high quality cultivar; while the other widely grown cultivars (i.e., Gonen-98 and Kasifbey-95) were inferior to some other genotypes (Selimiye, Zajecarska-75, Guadelupe) in terms of yield and quality, under the conditions of experimental years.
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    Öğe
    Using Leaf Based Hyperspectral Models for Monitoring Biochemical Constituents and Plant Phenotyping in Maize
    (Tarbiat Modares Univ, 2016) Kahriman, F.; Demirel, K.; Inalpulat, M.; Egesel, C. O.; Genc, L.
    The aim of this study was to develop and validate qualitative and quantitative models to discriminate different types of maize and also estimate biochemical constituents. Spectral data were taken from the central leaf of randomly-chosen plants grown in field trials in 2011 and 2012. Leaf chlorophyll and protein content and stalk protein content were determined in the same plants. Four different Support Vector Machine (SVM) models were generated and validated in this study. In qualitative models, maize type was designated as dependent variable while Full Spectral (FS) data (400-1,000 nm) and Spectral Indices (SI) data (34 indices/bands) were independent variables. In the two quantitative models (SVMR-FS and SVMR-SI), independent variables were the same, whereas dependent variables were assigned as the quantitatively measured traits. Results showed the qualitative models to be a robust method of classification for distinguishing different maize types, such as High Oil Maize (HOM), High Protein Maize (HPM) and standard (NORMAL) maize genotypes. The SVMC-FS model was superior to SVMC-SI in terms of the genotypic classification of maize plants. Quantitative models with full spectral data gave more robust prediction than the others. The best prediction result (RMSEC=222.4 mu g g(-1), R-2 for Cal=0.739, SEP=213.3 mu g g(-1); RPD=2.04 and r=0.877) was obtained from the SVMR-FS model developed for chlorophyll content. Indirect estimation models, based on relationships between leaf-based spectral measurements and leaf and stalk protein content, were less satisfactory.