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Year 2016, Volume: 4 Issue: Special Issue-1, 180 - 184, 25.12.2016
https://doi.org/10.18201/ijisae.270632

Abstract

References

  • 1. Ebrahimi, E., K. Mollazade, and S. Babaei, Toward an automatic wheat purity measuring device: A machine vision-based neural networks-assisted imperialist competitive algorithm approach. Measurement, 2014. 55: p. 196-205.
  • 2. Fengnong Chen, F.C. Wheat image correction for feature segmentation Based on color Linear CCD. in Digital Manufacturing and Automation (ICDMA). 2010. ChangSha: IEEE.
  • 3. Xia, X.F., Chao; Lu, Shu-Jie; Hou, Li-Long. The Analysis of Wheat Appearance Quality Based on Digital Image Processing. in Environmental Science and Information Application Technology. 2010. Wuhan: IEEE.
  • 4. Pourreza, A., et al., Identification of nine Iranian wheat seed varieties by textural analysis with image processing. Computers and Electronics in Agriculture, 2012. 83: p. 102-108.
  • 5. Ece Olcay Güneş, S.A., Mürvet Kırcı, Amir Kalateh, Yüksel Çakır. Determination of the varieties and characteristics of wheat seeds grown in Turkey using image processing techniques. in Third International Conference on Agro-geoinformatics 2014. Beijing: IEEE.
  • 6. Babalik, A., et al., Effects of Feature Selection Using Binary Particle Swarm Optimization on Wheat Variety Classification. Advances in Information Technology, 2010. 114: p. 11-17.
  • 7. Farahani, L., Discrimination of some cultivars of durum wheat (Triticum durum Desf.) using image analysis. International Research Journal of Applied and Basic Sciences, 2012. 3(7): p. 1375-1380.
  • 8. Manickavasagan, A., et al., Wheat class identification using monochrome images. Journal of Cereal Science, 2008. 47(3): p. 518-527.
  • 9. Williams, K., J. Munkvold, and M. Sorrells, Comparison of digital image analysis using elliptic Fourier descriptors and major dimensions to phenotype seed shape in hexaploid wheat (Triticum aestivum L.). Euphytica, 2013. 190(1): p. 99-116.
  • 10. Paliwal, J., et al., Cereal grain and dockage identification using machine vision. Biosystems Engineering, 2003. 85(1): p. 51-57.
  • 11. FN Chen, F.C., YB Ying. Detect black germ in wheat using machine vision. in International Conference on Computer Distributed Control and Intelligent Environmental Monitoring (CDCIEM). 2011. Changsha: IEEE.
  • 12. Technologies, A.V. Prosilica_GT_TechMan.pdf. 2016 [cited 2016 1 Aug 2016]; Available from: https://cdn.alliedvision.com/fileadmin/content/documents/products/cameras/Prosilica_GT/techman/Prosilica_GT_TechMan.pdf.
  • 13. Qin, Y.B., et al., Extended-Maxima Transform Watershed Segmentation Algorithm for Touching Corn Kernels. Advances in Mechanical Engineering, 2013.
  • 14. Kamran Ali, A.J., M. Umair Gull, Mustansar Fiaz. Medical Image Segmentation Using H-minima Transform and Region Merging Technique. in Frontiers of Information Technology (FIT), 2011. 2011. Islamabad: IEEE.
  • 15. R.Kiruthika, S.M., Azha Periasamy, Matching Of Different Rice Grains Using Digital Image Processing. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2013. 2(7): p. 2937-2941.
  • 16. MathWorks. regionprops. 2016 [cited 2016 02.08.2016]; Available from: http://www.mathworks.com/help/images/ref/regionprops.html.

Separation of Wheat Seeds from Junk in a Dynamic System Using Morphological Properties

Year 2016, Volume: 4 Issue: Special Issue-1, 180 - 184, 25.12.2016
https://doi.org/10.18201/ijisae.270632

Abstract

Wheat is the main food source of the humankind. After its harvest, it
goes through many procedures from its separation from chaff to its packaging.
With the development in technology, many of these procedures are realized with
automatic systems which saves the manufacturer the cost of labour, time and
provides the customer with more quality food. One of the main concerns of
quality food production is to provide a customer with the product in its purest
form which means the product must be separated from all foreign matters. In
this study, type-1252 durum wheat seeds have been separated from junk using the
morphological properties of wheat seeds through the uncompressed video image taken
with the camera Prosilica GT2000c. The main references for the quality measurement
of wheat seeds are the shape and the dimensions of a wheat seed. Aiming for
high quality wheat grain storage with no junk, this article has adopted various
image processing techniques from image preprocessing to feature extraction. The
image processing has been realized in a computer environment and the results show
that the image processing is successful and the detection of wheat seeds from
junk was accurate.

References

  • 1. Ebrahimi, E., K. Mollazade, and S. Babaei, Toward an automatic wheat purity measuring device: A machine vision-based neural networks-assisted imperialist competitive algorithm approach. Measurement, 2014. 55: p. 196-205.
  • 2. Fengnong Chen, F.C. Wheat image correction for feature segmentation Based on color Linear CCD. in Digital Manufacturing and Automation (ICDMA). 2010. ChangSha: IEEE.
  • 3. Xia, X.F., Chao; Lu, Shu-Jie; Hou, Li-Long. The Analysis of Wheat Appearance Quality Based on Digital Image Processing. in Environmental Science and Information Application Technology. 2010. Wuhan: IEEE.
  • 4. Pourreza, A., et al., Identification of nine Iranian wheat seed varieties by textural analysis with image processing. Computers and Electronics in Agriculture, 2012. 83: p. 102-108.
  • 5. Ece Olcay Güneş, S.A., Mürvet Kırcı, Amir Kalateh, Yüksel Çakır. Determination of the varieties and characteristics of wheat seeds grown in Turkey using image processing techniques. in Third International Conference on Agro-geoinformatics 2014. Beijing: IEEE.
  • 6. Babalik, A., et al., Effects of Feature Selection Using Binary Particle Swarm Optimization on Wheat Variety Classification. Advances in Information Technology, 2010. 114: p. 11-17.
  • 7. Farahani, L., Discrimination of some cultivars of durum wheat (Triticum durum Desf.) using image analysis. International Research Journal of Applied and Basic Sciences, 2012. 3(7): p. 1375-1380.
  • 8. Manickavasagan, A., et al., Wheat class identification using monochrome images. Journal of Cereal Science, 2008. 47(3): p. 518-527.
  • 9. Williams, K., J. Munkvold, and M. Sorrells, Comparison of digital image analysis using elliptic Fourier descriptors and major dimensions to phenotype seed shape in hexaploid wheat (Triticum aestivum L.). Euphytica, 2013. 190(1): p. 99-116.
  • 10. Paliwal, J., et al., Cereal grain and dockage identification using machine vision. Biosystems Engineering, 2003. 85(1): p. 51-57.
  • 11. FN Chen, F.C., YB Ying. Detect black germ in wheat using machine vision. in International Conference on Computer Distributed Control and Intelligent Environmental Monitoring (CDCIEM). 2011. Changsha: IEEE.
  • 12. Technologies, A.V. Prosilica_GT_TechMan.pdf. 2016 [cited 2016 1 Aug 2016]; Available from: https://cdn.alliedvision.com/fileadmin/content/documents/products/cameras/Prosilica_GT/techman/Prosilica_GT_TechMan.pdf.
  • 13. Qin, Y.B., et al., Extended-Maxima Transform Watershed Segmentation Algorithm for Touching Corn Kernels. Advances in Mechanical Engineering, 2013.
  • 14. Kamran Ali, A.J., M. Umair Gull, Mustansar Fiaz. Medical Image Segmentation Using H-minima Transform and Region Merging Technique. in Frontiers of Information Technology (FIT), 2011. 2011. Islamabad: IEEE.
  • 15. R.Kiruthika, S.M., Azha Periasamy, Matching Of Different Rice Grains Using Digital Image Processing. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2013. 2(7): p. 2937-2941.
  • 16. MathWorks. regionprops. 2016 [cited 2016 02.08.2016]; Available from: http://www.mathworks.com/help/images/ref/regionprops.html.
There are 16 citations in total.

Details

Subjects Engineering
Journal Section Research Article
Authors

Esra Kaya

İsmail Sarıtaş

İlker Ali Özkan

Publication Date December 25, 2016
Published in Issue Year 2016 Volume: 4 Issue: Special Issue-1

Cite

APA Kaya, E., Sarıtaş, İ., & Özkan, İ. A. (2016). Separation of Wheat Seeds from Junk in a Dynamic System Using Morphological Properties. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 180-184. https://doi.org/10.18201/ijisae.270632
AMA Kaya E, Sarıtaş İ, Özkan İA. Separation of Wheat Seeds from Junk in a Dynamic System Using Morphological Properties. International Journal of Intelligent Systems and Applications in Engineering. December 2016;4(Special Issue-1):180-184. doi:10.18201/ijisae.270632
Chicago Kaya, Esra, İsmail Sarıtaş, and İlker Ali Özkan. “Separation of Wheat Seeds from Junk in a Dynamic System Using Morphological Properties”. International Journal of Intelligent Systems and Applications in Engineering 4, no. Special Issue-1 (December 2016): 180-84. https://doi.org/10.18201/ijisae.270632.
EndNote Kaya E, Sarıtaş İ, Özkan İA (December 1, 2016) Separation of Wheat Seeds from Junk in a Dynamic System Using Morphological Properties. International Journal of Intelligent Systems and Applications in Engineering 4 Special Issue-1 180–184.
IEEE E. Kaya, İ. Sarıtaş, and İ. A. Özkan, “Separation of Wheat Seeds from Junk in a Dynamic System Using Morphological Properties”, International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. Special Issue-1, pp. 180–184, 2016, doi: 10.18201/ijisae.270632.
ISNAD Kaya, Esra et al. “Separation of Wheat Seeds from Junk in a Dynamic System Using Morphological Properties”. International Journal of Intelligent Systems and Applications in Engineering 4/Special Issue-1 (December 2016), 180-184. https://doi.org/10.18201/ijisae.270632.
JAMA Kaya E, Sarıtaş İ, Özkan İA. Separation of Wheat Seeds from Junk in a Dynamic System Using Morphological Properties. International Journal of Intelligent Systems and Applications in Engineering. 2016;4:180–184.
MLA Kaya, Esra et al. “Separation of Wheat Seeds from Junk in a Dynamic System Using Morphological Properties”. International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. Special Issue-1, 2016, pp. 180-4, doi:10.18201/ijisae.270632.
Vancouver Kaya E, Sarıtaş İ, Özkan İA. Separation of Wheat Seeds from Junk in a Dynamic System Using Morphological Properties. International Journal of Intelligent Systems and Applications in Engineering. 2016;4(Special Issue-1):180-4.