Ismail Kavdir, Ph.D.

Affiliations: 
2000 Michigan State University, East Lansing, MI 
Area:
Agricultural Engineering, Computer Science
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"Ismail Kavdir"

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Daniel E. Guyer grad student 2000 Michigan State
 (Apple sorting using neural networks, statistical classifiers and spectral reflectance imaging.)
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Publications

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Kurtulmuş F, Öztüfekçi S, Kavdır İ. (2018) Classification of chestnuts according to moisture levels using impact sound analysis and machine learning Journal of Food Measurement and Characterization. 12: 2819-2834
Kavdır İ, Büyükcan MB, Kurtulmuş F. (2018) Classification of olives using FT-NIR spectroscopy, neural networks and statistical classifiers Journal of Food Measurement and Characterization. 12: 2493-2502
Buyukcan MB, Kavdir I. (2017) Prediction of some internal quality parameters of apricot using FT-NIR spectroscopy Journal of Food Measurement and Characterization. 11: 651-659
Kurtulmuş F, Alibaş İ, Kavdır I. (2016) Classification of pepper seeds using machine vision based on neural network International Journal of Agricultural and Biological Engineering. 9: 51-62
Donis-González IR, Guyer DE, Kavdir I, et al. (2015) Development and applicability of an agarose-based tart cherry phantom for computer tomography imaging Journal of Food Measurement and Characterization. 9: 290-298
Kurtulmuş F, Kavdir I. (2014) Detecting corn tassels using computer vision and support vector machines Expert Systems With Applications. 41: 7390-7397
Kavdir I, Buyukcan MB, Lu R, et al. (2009) Prediction of olive quality using FT-NIR spectroscopy in reflectance and transmittance modes Biosystems Engineering. 103: 304-312
Kavdir I, Guyer DE. (2008) Evaluation of different pattern recognition techniques for apple sorting Biosystems Engineering. 99: 211-219
Kavdir I, Lu R, Ariana D, et al. (2007) Visible and near-infrared spectroscopy for nondestructive quality assessment of pickling cucumbers Postharvest Biology and Technology. 44: 165-174
Kavdir I. (2004) Discrimination of sunflower, weed and soil by artificial neural networks Computers and Electronics in Agriculture. 44: 153-160
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