Digital image processing for quality ranking of saffron peach
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Department of Farm Machinery, College of Agriculture, Isfahan University of Technology, Isfahan, 84156-83111, Iran
College of Agriculture, Shahrekord University, Shahrekord, 115, Iran
College of Engineering, Isfahan University, Isfahan, 81744, Iran
Int. Agrophys. 2010, 24(2): 115-120
A machine vision system was introduced for the evaluation and classification of the Iranian saffron peach. Physical features such as size and colour were measured to categorize peaches into three quality classes of red-yellow, yellow-red, and yellow. The HIS model hue, saturation, and luminance was used for colour processing of flawless samples and four boundaries were selected for the peach size image. The colours of peaches estimated by the system were well correlated with the colorimetric index values that are currently used as standards. Experimental results are promising and suggest that using the USDA standard, the size classification accuracy achieved is almost 96%, while the colour classification accuracy is around 90%, and that the spot detection algorithm performs well with correct detection levels of 97 and 85% for brown and white skin spots, respectively.
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