Now showing items 1-3 of 3

    • Fast insect damage detection in wheat kernels using transmittance images 

      Çataltepe, Z.; Pearson, T.; Cetin, A. Enis (IEEE, 2004-07)
      We used transmittance images and different learning algorithms to classify insect damaged and un-damaged wheat kernels. Using the histogram of the pixels of the wheat images as the feature, and the linear model as the ...
    • Inter-varietal structural variation in grapevine genomes 

      Cardone, M. F.; D'Addabbo, P.; Alkan C.; Bergamini, C.; Catacchio, C. R.; Anaclerio, F.; Chiatante, G.; Marra, A.; Giannuzzi, G.; Perniola, R.; Ventura M.; Antonacci, D. (Wiley-Blackwell Publishing Ltd., 2016)
      Grapevine (Vitis vinifera L.) is one of the world's most important crop plants, which is of large economic value for fruit and wine production. There is much interest in identifying genomic variations and their functional ...
    • Wheat and hazelnut inspection with impact acoustics time-frequency patterns 

      İnce, N. F.; Onaran, İbrahim; Tewfik, A. H.; Kalkan, H.; Pearson, T.; Çetin, A. Enis; Yardimci, Y. (ASABE, 2007-06)
      Kernel damage caused by insects and fungi is one of the most common reason for poor flour quality. Cracked hazelnut shells are prone to infection by cancer producing mold. We propose a new adaptive time-frequency classification ...