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Comparison some physical properties of six varieties of wheat seeds using image processing

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Research Paper 01/06/2015
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Comparison some physical properties of six varieties of wheat seeds using image processing

Salah Ghamari, Saber Nemati, Reza Talebi, Abdolvahed Khanahmadzadeh
J. Bio. Env. Sci.6( 6), 317-323, June 2015.
Certificate: JBES 2015 [Generate Certificate]
Key: ColorDimensionImage processingWheat variety

Abstract

Automated computer methods which utilize high-speed image capturing and data processing are the most advanced methods, providing a high degree of accuracy in seed quality testing and sorting. In order to present a quick and accurate method for measuring physical properties, the image processing technique was used to characterize the physical properties of six bread wheat genotypes (Azar2, Gaskozhen, MD, Pishgam, Sainoz and Sardari). From each variety, 100 seeds were selected randomly and high quality images of them were acquired. Feature extraction of images, including dimensions, projected area and color of them was down. The results showed that the sardari and sainoz varieties had the maximum and minimum values of seed length. Also for the width and projected area of seeds, maximum and minimum values belong to pishgam and sainoz varieties, respectively.The sardari and MD varieties respectively presented the high and low mean values of R, G and the maximum and minimum values for B belong to sainoz and MD varieties, respectively. These results can be useful in recognition and classification of wheat varieties.

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Altuntas E, Yildiz M. 2007. Effect of moisture content on some physical and mechanical properties of faba bean (ViciafabaL) grains. Journal of Food Engineering 78, 174-183.

Amin MN, Hossain MA, Roy KC. 2004. Effects of moisture content on some physical Properties of lentil seeds. Journal of Food Engineering 65, 83-87.

Arefi A, Modarres-Motlagh A, Farrokhi-Teimourlou R. 2011. Wheat class identification using computer vision system and artificial neural networks. International Agrophysics 25(4), 319-323.

Baümler E, Cuniberti A, Nolasco SM, Riccobene IC. 2006. Moisture- dependent physical and compression properties of safflower seed. Journal of Food Engineering 79, 134–140.

Coskuner Y, Karababa E. 2007. Physical properties of coriander seeds (Coriandrumsativum L). Journal of Food Engineering 80, 408-416.

Esref I, Hulya I. 2008. The effect of moisture of organic chickpea grain on the physical and mechanical properties. International Journal of Agricultural Research 3, 40-51.

Ghamari S, Mohammadi KH, Khanahmadzadeh AV, Goli H. 2014. Evaluation the Some Physical Properties of Chickpea Seeds in Kurdistan Region of Iran. International Journalof Agricultureand Forestry 4(3A), 4-7.

Gonzalez RC, Woods RE. 1992. Digital Image Processing. Boston, Mass Addison–Wesley.

Guevara-Hernandez F, Gomez-Gil J. 2011. A machine vision system for classification of wheat and barley grain kernels. Spanish Journal of Agricultural Research 9(3), 672-680.

Gürsoy S, Güzel E. 2010. Determination of Physical Properties of Some Agricultural Grains.Research Journal of Applied Sciences, Engineering and Technology 2, 492-498.

Jouki M, Khazaei N. 2012. Some Physical Properties of Rice Seed (Oryza sativa). Research Journal of Applied Sciences, Engineering and Technology 4(13), 1846-1849.

Karababa E. 2006. Physical properties of popcorn kernels. Journal of Food Engineering 72, 100-107.

Khoshroo A, Arefi A, Masoumiasl A, Jowkar GH. 2014. Classification of Wheat Cultivars Using Image Processing and Artificial Neural Networks. Agricultural Science Communications 2(1), 17-22.

Kilic K, Boyaci IH, Koksel H, Kusmengoglu I. 2007. A classification system for beans using computer vision system and artificial neural networks. Journal of Food Engineering 78, 897–904.

Najafabadi SSM, Farahani L. 2012. Shape analysis of common bean (Phaseolus vulgaris L.) seeds using image analysis. International Research Journal of Applied & Basic Sciences 3(8), 1619-1623.

Pazoki A, Pazoki Z. 2011. Classification system for rain fed wheat grain cultivars using artificial neural network. African Journal of Biotechnology 10(41), 8031-8038.

Punn M, Bhalla N. 2013. Classification of Wheat Grains Using Machine Algorithms. International Journal of Scientific Research 2(8), 2319-7064.

Razavi SMA, Bostan A, Rezaie M. 2010. Image processing and physico-mechanical Properties of basil seed (ocimumbasilicum). Journal of Food Process Engineering 33, 51–64.

Sacilik K, Ozturk R, Keskin R. 2003. Some physical properties of hemp seed. Biosystems Engineering 86, 191-198.

Selvi KC, Pinar Y, Yeşiloğlu E. 2006. Some physical properties of linseed. Biosystems Engineering 95(4), 607–612.

Suo X M, Jiang YT, Yang M, Li SK, Wang KR, Wang CT. 2010. Artificial neural network to predict leaf population chlorophyll content from cotton plant images, Agricultural Sciencein China 9(1), 38–45.

Varma VS, Durga K, Keshavulu K. 2013. Seed image analysis: its applications in seed science research, International Research Journal AgriculturalScience 1(2), 30-36.

Voicu G, Tudosie EM, Ungureanu N, Constantin GA. 2013. Some mechanical characteristics of wheat seeds obtained by uniaxial compression tests. Scientific Bulletin of University Politehnica of Buchares 75(4), 265-278.

Wang N, Dowell FE, Zhang N. 2003.Determining wheat vitreousness using image processing and a neural network. Transaction of the ASAE 46(4), 1143–1150.

Yimyam P, Chalidabhongse T, Sirisomboon P, Boonmung S. 2005. Physical Properties Analysis of Mango using Computer Vision. ICCAS2005 June 2-5 KintexGyeonggi-Do Korea.

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