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

Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 180 - 184, 25.12.2016

Esra Kaya İsmail Sarıtaş İlker Ali Özkan

https://doi.org/10.18201/ijisae.270632

Öz

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.

Anahtar Kelimeler

Blob Analysis, Feature Extraction, Image Processing, Junk, Morphological Properties, Segmentation, Wheat Seed

Kaynakça

• 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.