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

Paper Details

Research Paper 01/06/2015
Views (959)
current_issue_feature_image
publication_file

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

Salah Ghamari, Saber Nemati, Reza Talebi, Abdolvahed Khanahmadzadeh
J. Biodiv. & Environ. Sci. 6(6), 317-323, June 2015.
Copyright Statement: Copyright 2015; The Author(s).
License: CC BY-NC 4.0

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.

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.

Related Articles

In vitro assessment of Bambara groundnut M3 mutant genotypes for resistance to Macrophomina phaseolina (Tassi) Goid. in the seedling stage in Burkina Faso

Brahime Tingueri*, Souleymane Ouattara, Adjima Ouoba, Romain W. Soalla, Mahamadi Hamed Ouedraogo, J. Biodiv. & Environ. Sci. 28(6), 141-149, June 2026.

Impact of Beauveria bassiana and Metarhizium anisopliae on biochemical and antioxidant enzymes in Rhynchophorus ferrugineus (Olivier) infesting oil palm

M. Malarvizhi, N. Santhana Bharathi, K. Sujatha*, A. Vijaya Anand, R. Manikandan, J. P. Antony Prabhu, J. Biodiv. & Environ. Sci. 28(6), 129-140, June 2026.

Typhoon risk perception and preparedness after Sendong in Bayug Island

Dinah Millendez*, Lex Rei Brendon Hilario, Jay Rey Alovera, Elizabeth Edan Albiento, Melgie Alas, Peter Suson, J. Biodiv. & Environ. Sci. 28(6), 120-128, June 2026.

Floristic composition and woody species diversity in Campo-Ma’an National Park, South Cameroon

Achey Nkenfack Djike Baudelair*, Temgoua Lucie Félicité, Kuete Fogang Marcien, Nfondem Poumie Mohamed Mounir, Atoupka Abdel Malik, Djeuni Duplex Romuald, Kontchiachou Nkana Didier, J. Biodiv. & Environ. Sci. 28(6), 103-119, June 2026.

Comparative effects of bio-inoculant on nutrient dynamics of biodegradable waste

Anjelle-J G. Debosura*, Carlo Stephen O. Moneva, Corazon V. Ligaray, Elizabeth Edan M. Albiento, MA. Cecilia V. Almeda, Melgie A. Alas, Frandel Louis S. Dagoc, Peter D. Suson, J. Biodiv. & Environ. Sci. 28(6), 97-102, June 2026.

Impact of deforestation on the aquatic macroinvertebrate community and the ecological quality of Mé River (South-East, Côte d’Ivoire)

Gnago Dohou Affri*, Tapé Logboh David, Edia Oi Edia, J. Biodiv. & Environ. Sci. 28(6), 80-96, June 2026.

Vulnerability and regeneration potential of Bambusa vulgaris in Ebolowa, South Cameroon

Rodine Tchiofo Lontsi*, Duchesse Elvira Kepmou, Emilienne Laure Ngahane, Jacques Christophe Awoa Essam, Isaac Blaise Djoko, J. Biodiv. & Environ. Sci. 28(6), 68-79, June 2026.

Temporal availability of floral resources for the honey bee (Apis mellifera) in a forest ecosystem in the sudanian zone of Côte d’Ivoire: The case of Badenou classified forest

Dofoungo Koné*, Comlan Mawussi Koudegnan, Siendou Coulibaly, Fofana Séguéna, Bruno Marcel Iritié, Wandan Eboua Narcisse, J. Biodiv. & Environ. Sci. 28(6), 56-67, June 2026.