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      • Department of Electrical and Electronics Engineering
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      Detection of fungal damaged popcorn using image property covariance features

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      Author
      Yorulmaz, O.
      Pearson, T. C.
      Çetin, A.
      Date
      2012
      Source Title
      Computers and Electronics in Agriculture
      Print ISSN
      0168-1699
      Publisher
      Elsevier
      Volume
      84
      Pages
      47 - 52
      Language
      English
      Type
      Article
      Item Usage Stats
      157
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      101
      downloads
      Abstract
      Covariance-matrix-based features were applied to the detection of popcorn infected by a fungus that causes a symptom called " blue-eye" . This infection of popcorn kernels causes economic losses due to the kernels' poor appearance and the frequently disagreeable flavor of the popped kernels. Images of kernels were obtained to distinguish damaged from undamaged kernels using image-processing techniques. Features for distinguishing blue-eye-damaged from undamaged popcorn kernel images were extracted from covariance matrices computed using various image pixel properties. The covariance matrices were formed using different property vectors that consisted of the image coordinate values, their intensity values and the first and second derivatives of the vertical and horizontal directions of different color channels. Support Vector Machines (SVM) were used for classification purposes. An overall recognition rate of 96.5% was achieved using these covariance based features. Relatively low false positive values of 2.4% were obtained which is important to reduce economic loss due to healthy kernels being discarded as fungal damaged. The image processing method is not computationally expensive so that it could be implemented in real-time sorting systems to separate damaged popcorn or other grains that have textural differences.
      Keywords
      Correlation features
      Covariance features
      Fungus detection on popcorn kernels
      Image processing
      SVM
      Color channels
      Correlation features
      Covariance features
      Covariance matrices
      Economic loss
      False positive
      Image coordinates
      Image pixels
      Image processing - methods
      Image properties
      Intensity values
      Kernel image
      Recognition rates
      Second derivatives
      Sorting system
      Support vector machine (SVM)
      SVM
      Image processing
      Losses
      Support vector machines
      Covariance matrix
      Computer simulation
      Correlation
      Crop damage
      Detection method
      Fungal disease
      Fungus
      Image analysis
      Maize
      Matrix
      Pixel
      Variance analysis
      Fungi
      Nemophila
      Pseudomugilidae
      Permalink
      http://hdl.handle.net/11693/21454
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.compag.2012.02.012
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      • Department of Electrical and Electronics Engineering 3597
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