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We developed a personal computer–based machine vision system and applied it in computer-aided potato inspection. The system was able to classify 50 potato images per second for potato weight, cross-sectional diameter, shape, and color, which are the important criteria in sorting potatoes in practice. An ellipse was used as the shape descriptor for potato shape inspection and color thresholding was performed in the hue-saturation-value (HSV) color space to detect green color defects. Our machine vision algorithms were evaluated objectively for weight and cross-sectional diameter inspection, and subjectively for shape and color inspection on 200 potatoes of three varieties. In addition, a subjective evaluation experiment was carried out with six United States Department of Agriculture–licensed professional inspectors participating. The average success rate was 91.2% for weight inspection and 88.7% for diameter inspection. The shape and color inspection algorithms achieved 85.5% and 78.0% success rates, respectively. The overall success rate, combining all of the above criteria, was 86.5%. This type of machine vision system can be reliably used in the future to sort out the definitely good and bad potatoes and to forward the rest for human sorting, thus potentially reducing manual efforts significantly. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 423–433, 1998

PC-based machine vision system for real-time computer-aided potato inspection