Browsing by Subject "Crops"
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Item Open Access Fast insect damage detection in wheat kernels using transmittance images(IEEE, 2004-07) Çataltepe, Z.; Pearson, T.; Cetin, A. EnisWe 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 learning algorithm, we achieved a False Positive Rate (1-specificity) of 0.12 at the True Positive Rate (sensitivity) of 0.8 and an Area Under the ROC Curve (AUC) of 0.90 ± 0.02. Combining the linear model and a Radial Basis Function Network in a committee resulted in a FP Rate of 0.09 at the TP Rate of 0.8 and an AUC of 0.93 ± 0.03.Item Open Access Inter-varietal structural variation in grapevine genomes(Wiley-Blackwell Publishing Ltd., 2016) 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.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 effects on inter-varietal, phenotypic differences. Using an approach developed for the analysis of human and mammalian genomes, which combines high-throughput sequencing, array comparative genomic hybridization, fluorescent in�situ hybridization and quantitative PCR, we created an inter-varietal atlas of structural variations and single nucleotide variants (SNVs) for the grapevine genome analyzing four economically and genetically relevant table grapevine varieties. We found 4.8 million SNVs and detected 8% of the grapevine genome to be affected by genomic variations. We identified more than 700 copy number variation (CNV) regions and more than 2000 genes subjected to CNV as potential candidates for phenotypic differences between varietiesItem Open Access Wheat and hazelnut inspection with impact acoustics time-frequency patterns(ASABE, 2007-06) İnce, N. F.; Onaran, İbrahim; Tewfik, A. H.; Kalkan, H.; Pearson, T.; Çetin, A. Enis; Yardimci, Y.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 procedure for detecting cracked hazelnut shells and damaged wheat kernels using impact acoustic emissions recorded by dropping wheat kernels or hazelnut shells on a steel plate. The proposed algorithm is based on a flexible local discriminant bases (F-LDB) procedure. The F-LDB method combines local cosine packet analysis and a frequency axis clustering approach which supports individual time and frequency band adaptation. Discriminant features are extracted from the adaptively segmented acoustic signal, sorted according to a Fisher class separability criterion, post processed by principal component analysis and fed to linear discriminant. We describe experimental results that establish the superior performance of the proposed approach when compared with prior techniques reported in the literature or used in the field. Our approach achieved classification accuracy in paired separation of undamaged wheat kernels from IDK, Pupae and Scab damaged kernels with 96%, 82% and 94%. For hazelnuts the accuracy was 97.1%.