Cereal Grain Classification by Optimal Features and Intelligent Classifiers
Keywords:
morphological, colour, wavelet transform, neural networks, statistical classifier, fuzzy logicAbstract
The present paper focused on the classification of cereal grains using different classifiers combined to morphological, colour and wavelet features. The grain types used in this study were Hard Wheat, Tender Wheat and Barley. Different types of features (morphological, colour and wavelet) were extracted from colour images using different approaches. They were applied to different classification methods.References
M. Abdellaoui, A. Douik, M. Annabi, Détérmination des critéres de forme et de couleur pour la classification des grains de céréales, Proc. Nouvelles Tendances Technologiques en Génie Electrique et Informatique, GEI'2006, Hammamet,Tunisia, 2006, pp. 393-402.
D. A. Barker, T. A. Vouri, M. R. Hegedus, D. G. Myers, The use of ray parameters for the discrimination of Australian wheat varieties. Plant Varieties and Seeds 5(1) (1992) 35-45.
D. A. Barker, T. A. Vouri, M. R. Hegedus, D. G. Myers, The use of slice and aspect ratio parameters for the discrimination of Australian wheat varieties, Plant Varieties and Seeds 5(1) (1992) 47-52.
D. A. Barker, T. A. Vouri, M. R. Hegedus, D. G. Myers, The use of Fourier descriptors for the discrimination of Australian wheat varieties, Plant Varieties and Seeds 5(1) (1992) 93-102.
D. A. Barker, T. A. Vouri, M. R. Hegedus, D. G. Myers, The use of Chebychev coefficients for the discrimination of Australian wheat varieties, Plant Varieties and Seeds 5(1) (1992) 103-111.
P. D. Keefe. A dedicated wheat grain image analyzer, Plant Varieties and Seeds 5(1) (1992) 27-33.
H. D. Sapirstein, J. M. Kohler, Physical uniformity of graded railcar and vessel shipments of Canada Western Red Spring wheat determined by digital image analysis, Canadian Journal of Plant Science 75(2) (1995) 363-369. http://dx.doi.org/10.4141/cjps95-061
J. Paliwal, N. S. Shashidhar, D. S. Jayas, Grain kernel identification using kernel signature, Transactions of the ASAE 42(6) (1999) 1921-1924. http://dx.doi.org/10.13031/2013.13357
S. Majumdar, D. S. Jayas, Classification of cereal grains using machine vision. I. Morphology models, Transactions of the ASAE 43(6) (2000) 1669-1675. http://dx.doi.org/10.13031/2013.3107
M. Neuman, H. D. Sapirstein, E. Shwedyk, W. Bushuk, Wheat grain colour analysis by digital image processing: I. Methodology, Journal of Cereal Science 10(3) (1989) 175-182. http://dx.doi.org/10.1016/S0733-5210(89)80046-3
M. Neuman, H. D. Sapirstein, E. Shwedyk, W. Bushuk, Wheat grain colour analysis by digital image processing: II. Wheat class determination, Journal of Cereal Science 10(3) (1989) 182-183. http://dx.doi.org/10.1016/s0733-5210(89)80047-5
X. Y. Luo, D. S. Jayas, S. J. Symons, Identification of damaged kernels in wheat using a colour machine vision system. Journal of Cereal Science 30(1) (1999) 49-59. http://dx.doi.org/10.1006/jcrs.1998.0240
S. Majumdar, D. S. Jayas, Classification of cereal grains using machine vision. II. Color models, Transactions of the ASAE 43(6) (2000) 1677-1680. http://dx.doi.org/10.13031/2013.3067
S. Majumdar, D. S. Jayas, Classification of cereal grains using machine vision. III. Texture models, Transactions of the ASAE 43(6) (2000) 1681-1687. http://dx.doi.org/10.13031/2013.3068
S. Majumdar, D. S. Jayas, Classification of cereal grains using machine vision. IV. Combined morphology, color, and texture models, Transactions of the ASAE 43(6) (2000) 1689-1694. http://dx.doi.org/10.13031/2013.3069
J. Paliwal, N. S. Visen, D. S. Jayas, N. D. G. White, Comparison of a neural network and a nonparametric classifier for grain kernel identification, Biosystems Engineering, 85(4) (2003) 405-413. http://dx.doi.org/10.1016/S1537-5110(03)00083-7
N. S. Visen, D. S. Jayas, J. Paliwal, N. D. G. White, Comparison of two neural network architectures for classification of singulated cereal grains, Canadian Biosystems Engineering 46 (2004) 3.7-3.14.
M. Abdellaoui, A. Douik, M. Annabi, Hybrid method for cereal grain identification using morphological and color features, Proc. 13th IEEE International Conference on Electronics, Circuits, and Systems, (Nice, France, 2006), pp. 870-873.
R. Choudhary, J. Paliwal, D. S. Jayas, Classification of cereal grains using wavelet, morphological, colour, and textural features of non-touching kernel images, Biosystems engineering 99 (2008) 330 - 337. http://dx.doi.org/10.1016/j.biosystemseng.2007.11.013
A. Douik, M. Abdellaoui, Cereal varieties classification using wavelet techniques combined to multi-layer neural networks, Proc. 16th Mediterranean Conference on Control and Automation, (Ajaccio, France, 2008) pp1822-1827. http://dx.doi.org/10.1109/med.2008.4601997
S. G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7) (1989) 674-693. http://dx.doi.org/10.1109/34.192463
H. Freeman, On the encoding of arbitrary geometric configurations, IEEE Trans on Electr. Comput. 10 (1961) 260-268. http://dx.doi.org/10.1109/TEC.1961.5219197
D. Zhang, G. Lu, A Comparative Study on Shape Retrieval Using Fourier Descriptors with Different Shape Signatures, Proc. IEEE International Conference on Multimedia and Expo, (2001), pp. 1139- 1142.
Published
Issue
Section
License
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
