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