Potato Sorting Based on Size and Color in Machine Vision System

Roya Hasankhani, Hosein Navid

Abstract


Potato (Solanum tuberosum) is cultivated as a major food resource in some countries that have moderate climate. Manual sorting is labor intensive. Furthermore in mechanical sorting the crop damages is high, for this reason we must operate a system in which the crop damages would be diminished. For sorting of potatoes fast, accurate and less labor intensive modern techniques such as Machine vision is created. Machine vision system is one of the modern sorting techniques. The basis of this method is imaging of samples, analysis of the images, comparing them with a standard and finally decision making in acceptance or rejection of samples. In this research 110 numbers of potatoes from Agria variety were prepared. Samples were pre-graded based on quantitative, qualitative and total factors manually before sorting. Quantitative, qualitative and total sorting in Machine vision system was performed by improving images quality and extracting the best thresholds. The accuracy of total sorting was %96.823.


Full Text: PDF DOI: 10.5539/jas.v4n5p235

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

Journal of Agricultural Science ISSN 1916-9752 (Print) ISSN 1916-9760 (Online)

Copyright © Canadian Center of Science and Education

To make sure that you can receive messages from us, please add the 'ccsenet.org' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.