Discrimination of Wheat Grain Varieties Using Image Analysis and Multidimensional Analysis Texture of Grain Mass

REFERENCES

• Seyed , M.A. , Razavia , A. and Rahbaria , R. 2010 . Computer image analysis and physico-mechanical properties of wild sage seed (Salvia macrosiphon) . International Journal of Food Properties , 13 ( 2 ) : 308 – 316 .
   Web of Science ®Google Scholar
• Yong , H. , Xiaoli , L. and Yongni , S. 2007 . Fast discrimination of apple varieties using Vis/NIR spectroscopy . International Journal of Food Properties , 10 ( 1 ) : 9 – 18 .
   Web of Science ®Google Scholar
• Mahmoodia , M. , Khazaeia , J. and Narjes . “ Modeling of geometric size distribution of almond ” . In International Journal of Food Properties 2010 doi: 10.1080/10942910903501872
   Google Scholar
• Majumdar , S. and Jayas , D.S. 2000 . Classification of cereal grains using machine vision: I. Morphology models . American Society of Agricultural Engineering , 43 : 1669 – 1675 .
   Google Scholar
• Majumdar , S. and Jayas , D.S. 2000 . Classification of cereal grains using machine vision: III . Texture models. Morphology models. American Society of Agricultural Engineering , 43 : 1681 – 1687 .
   Google Scholar
• Majumdar , S. and Jayas , D.S. 2000 . Classification of cereal grains using machine vision: II. Color models. Morphology models . American Society of Agricultural Engineering , 43 : 1677 – 1680 .
   Google Scholar
• Majumdar , S. and Jayas , D.S. 2000 . Classification of cereal grains using machine vision: VI . Combined morphology, color, and texture models. American Society of Agricultural Engineering , 43 : 1689 – 1694 .
   Google Scholar
• Jayas , D.S. , Paliwal , J. and Visen , N.S. 2000 . Multi-layer neural networks for image analysis of agricultural products . Journal of Agricultural Engineering Research , 77 : 119 – 128 .
   Web of Science ®Google Scholar
• Visen , N.S. , Paliwal , J. , Jayas , D.S. and White , N.D.G. 2001 . Specialist neural networks for cereal grain classification . Biosystems Engineering , 82 : 151 – 159 .
   Web of Science ®Google Scholar
• Visen , N.S. , Shashidhar , N.S. , Paliwal , J. and Jayas , D.S. 2002 . Identification and segmentation of occluding groups of grain kernels in a grain sample image . Journal of Agricultural Engineering Research , 79 : 159 – 166 .
   Google Scholar
• Paliwal , J. , Visen , N.S. and Jayas , D.S. 2001 . Evaluation of neural network architectures for cereal classification using morphological features . Journal of Agricultural Engineering Research , 79 : 361 – 370 .
   Web of Science ®Google Scholar
• Paliwal , J. , Visen , N.S. , Jayas , D.S. and White , N.D.G. 2003 . Comparison of a neural network and non-parametric classifier for grain kernel identification . Biosystems Engineering , 85 : 405 – 413 .
   Web of Science ®Google Scholar
• Paliwal , J. , Visen , N.S. , Jayas , D.S. and White , N.D.G. 2003 . Cereal grain and dockage identification using machine vision . Biosystems Engineering , 85 : 51 – 57 .
   Web of Science ®Google Scholar
• Utku , H. 2000 . Application of the feature selection method to discriminate digitized wheat arieties . Journal of Food Engineering , 46 : 211 – 216 .
   Web of Science ®Google Scholar
• Venora , G. , Grillo , O. , Ravalli , C. and Cremonini , R. 2009 . Identification of Italian landraces of bean (Phaseolus vulgaris L.) using an image analysis system . Scientia Horticulturae , 121 : 410 – 418 .
   Web of Science ®Google Scholar
• Venora , G. , Grillo , O. and Saccone , R. 2009 . Quality assessment of durum wheat storage centres in Sicily: Evaluation of vitreous, starchy and shrunken kernels using an image analysis system . Journal Cereal Science , 49 : 429 – 440 .
   Web of Science ®Google Scholar
• Venora , G. , Grillo , O. , Saccone , R. and Ravalli , C. June 30–July 3 2008 . “ Speck evaluation on commercial spaghetti using an imaging system ” . In From Seed to Pasta: The Durum Wheat Chain , June 30–July 3 , Bologna : International Durum Wheat Symposium .
   Google Scholar
• Venora , G. , Grillo , O. , Shahin , M.A. and Symons , S.J. 2007 . Identification of Sicilian landraces and Canadian cultivars of lentil using image analysis system . Food Research International , 40 : 161 – 166 .
   Web of Science ®Google Scholar
• Klepaczko , A. 2006 . “ Zastosowanie algorytmów analizy skupień do selekcji cech dla zadań klasyfikacji cech dla zadań klasyfikacji wektorów danych ” . In Praca doktorska. Politechnika Łódzka, Instytut Elektroniki
   Google Scholar
• Hall , M. , Frank , E. , Holmes , G. , Pfahringer , B. , Reutemann , P. and Witten , I.H. 2009 . The WEKA data mining software: An update . SIGKDD Explorations , 11 ( 1 ) : 10 – 18 .
   Google Scholar
• Pudil , P. , Novovičova , J. and Kittler , J. 1994 . Floating search methods in feature selection . Pattern Recognition Letters , 15 : 1119 – 1125 .
   Web of Science ®Google Scholar
• Pudil , P. and Novovičova , J. 1998 . Novel methods for subset selection with respect to problem knowledge . Intelligent Systems , 13 : 66 – 74 .
   Google Scholar
• Pudil , P. and Somol , P. 2005 . Current feature selection techniques in statistical pattern . Advances in Intelligent and Soft Computing , 30 : 53 – 68 .
   Google Scholar
• Oh , I.S. , Lee , J.S. and Moon , B.R. 2004 . Hybrid genetic algorithms for feature selection . Institute of Electrical and Electronics Engineers Transactions on Pattern Analysis and Machine Intelligence , 26 : 1424 – 1437 .
   PubMed Web of Science ®Google Scholar
• Witten , I.H. and Frank , E. 2005 . Data Mining. Practical Machine Learning Tools and Techniques , Second , New York, USA: Elsevier .
   Google Scholar
• Szczypiński , P.M. , Strzelecki , M. , Materka , A. and Klepaczko , A. 2009 . MaZda-A software package for image texture analysis . Computer Methods and Programs in Biomedicine , 94 : 66 – 76 .
   PubMed Web of Science ®Google Scholar