PC-BASED MACHINE VISION SYSTEM FOR REAL-TIME COMPUTER-AIDED POTATO INSPECTION

We developed a personal computer-based machine vision system and appli ed it in computer-aided potato inspection. The system was able to clas sify 50 potato images per second for potato weight, cross-sectional di ameter, 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 hu e-saturation-value (HSV) color space to detect green color defects. Ou r machine vision algorithms were evaluated objectively for weight and cross-sectional diameter inspection, and subjectively for shape and co lor inspection on 200 potatoes of three varieties. in addition, a subj ective evaluation experiment was carried out with six United States De partment of Agriculture-licensed professional inspectors participating . The average success rate was 91.2% for weight inspection and 88.7% f or diameter inspection. The shape and color inspection algorithms achi eved 85.5% and 78.0% success rates, respectively. The overall success rate, combining all of the above criteria, was 86.5%. This type of mac hine 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 sor ting, thus potentially reducing manual efforts significantly. (C) 1998 John Wiley & Sons, Inc.