Grading of Mushrooms Using a Machine Vision System

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Abstract
The quality features of the common white Agaricus bisporus mushroom were quantified using image analysis in order to inspect and grade the mushrooms by an automated system. The features considered were color, shape, stem cut, and cap veil opening. Two human inspectors evaluated samples which were divided into training and test sets. The vision system was trained to classify mushrooms into two quality grades using thresholding. The human inspection results were compared with each other as well as the computer vision system results. Misclassification by the vision system ranged from 8 to 56% depending upon the quality feature evaluated, but averaged about 20%. The disagreement between inspectors ranged from 14 to 36%.

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