Modern Approaches for Grain Quality Analysis and Assessment

  • M. Mladenov
  • M. Deyanov
  • S. Penchev
Part of the Studies in Computational Intelligence book series (SCI, volume 586)


The paper presents the approaches, methods and tools for assessment of main quality features of grain samples which are based on color image and spectra analyses. Visible features like grain color, shape, and dimensions are extracted from the object images. Information about object color and surface texture is obtained from the object spectral characteristics. The categorization of the grain sample elements in three quality groups is accomplished using two data fusion approaches. The first approach is based on the fusion of the results about object color and shape characteristics obtained using image analysis only. The second approach fuses the shape data obtained by image analysis and the color and surface texture data obtained by spectra analysis. The results obtained by the two data fusion approaches are compared.


Linear Discriminant Analysis Object Color Texture Model Quadratic Discriminant Analysis Sample Element 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



A big part of the analyses and results presented in this investigation are a part of implementation of the research project “Intelligent Technologies for Assessment of Quality and Safety of Food Agricultural Products”, funded by the Bulgarian National Science Fund.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Automatics and Mechatronics, Faculty of Electrotechnics, Electronics and AutomaticsUniversity of RousseRousseBulgaria

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