Software Application in Machine Vision Investigation of Agricultural Seeds Quality

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The paper tries to analyze the possibilities of classification of hemp fiber plant seeds in order to provide an image recognition sorting system that can estimate the quality of the fiber hemp seeds destined for human consumption. Systems that can optically grade cereal grains are already available in the agricultural industry. The automatic classification systems for seeds take in consideration the size, shape, color and texture in order to assess the quality of nutritional seeds.This work investigates the classification possibilities of image systems using photo images acquired by an RGB camera. The article presents the experimental stand and devices used for the image analysis of the fiber hemp seeds probes. The method for the selection of the representative pixels of the quality seeds is described and finally the results for the classification of the ratio of ripe seeds versus unripe seeds are presented in graphs. Conclusions are formulated and discussed.

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Edited by:

Adrian Olaru

Pages:

463-473

Citation:

D. Ola et al., "Software Application in Machine Vision Investigation of Agricultural Seeds Quality", Applied Mechanics and Materials, Vol. 436, pp. 463-473, 2013

Online since:

October 2013

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$41.00

[1] GRANITTO, Pablo M.; VERDES, Pablo F.; CECCATTO, H. Alejandro. Large-scale investigation of weed seed identification by machine vision. Computers and Electronics in Agriculture, 2005, 47. Jg., Nr. 1, S. 15-24.

DOI: https://doi.org/10.1016/j.compag.2004.10.003

[2] GRANITTO, Pablo M., et al. Weed seeds identification by machine vision. Computers and Electronics in Agriculture, 2002, 33. Jg., Nr. 2, S. 91-103. http: /dx. doi. org/10. 1016/S0168-1699(02)00004-2.

DOI: https://doi.org/10.1016/s0168-1699(02)00004-2

[3] WU, Ji-hua; LIU, Yan-de; OUYANG, Ai-guo. Research on Real Time Identification of Seed Variety by Machine Vision Technology [J]. Journal of Transcluction Technology, 2005, 4. Jg., S. 015.

[4] DELL'AQUILA, A. Towards new computer imaging techniques applied to seed quality testing and sorting. Seed Science and Technology, 2007, 35. Jg., Nr. 3, S. 519-538.

DOI: https://doi.org/10.15258/sst.2007.35.3.01

[5] SHAHIN, M. A.; SYMONS, S. J. A machine vision system for grading lentils. Canadian Biosystems Engineering, 2001, 43. Jg., S. 7. 7-7. 14.

[6] URENA, R.; RODRIGUEZ, F.; BERENGUEL, M. A machine vision system for seeds quality evaluation using fuzzy logic. Computers and Electronics in Agriculture, 2001, 32. Jg., Nr. 1, S. 1-20.

DOI: https://doi.org/10.1016/s0168-1699(01)00150-8

[7] MAJUMDAR, S.; JAYAS, D. S. Classification of bulk samples of cereal grains using machine vision. Journal of Agricultural Engineering Research, 1999, 73. Jg., Nr. 1, S. 35-47.

DOI: https://doi.org/10.1006/jaer.1998.0388

[8] LUO, X.; JAYAS, D. S.; SYMONS, S. J. Identification of damaged kernels in wheat using a color machine vision system. Journal of cereal science, 1999, 30. Jg., Nr. 1, S. 49-59.

DOI: https://doi.org/10.1006/jcrs.1998.0240

[9] . ] MATTHÄUS, B.; BRÜHL, L. Virgin hemp seed oil: An interesting niche product. Eur. J. Lipid Sci. Technol., 2008, 110 Jg., S. 655–661.

DOI: https://doi.org/10.1002/ejlt.200700311

[10] . ]Information on http: /www. agriculture. gov. sk. ca/Default. aspx?DN=e60e706d-c852-4206-9959-e4b134782175.