In this paper, two kinds of defects in Golden Delicious apples are recognized: bruise and russet. Russet is divided to two classes: russet in stem-end and russet out of stem-end. Apples are graded into three classes I, II and rejected, according to European standard. To
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In this paper, two kinds of defects in Golden Delicious apples are recognized: bruise and russet. Russet is divided to two classes: russet in stem-end and russet out of stem-end. Apples are graded into three classes I, II and rejected, according to European standard. To grade the apples, it is necessary to classify apple images into six classes: stem, calyx, bruise, russet in stem-end, russet out of stem-end and healthy. In this method, after pixel-based classification based on RGB color features by a perceptron neural network, correction in classification and stem detection is made. Hue and saturation features are used to correct the image regions classified to bruise. The correction of regions classified to calyx, russet in stem-end and russet out of stem-end is made based on the distance from the gravity center of the stem to the gravity center of each region. This paper presents a new method for defect classification and sub classification of russet to two classes, russet in stem-end and russet out of stem-end. Experimental results of the proposed algorithm show that the correct grading rate of 120 apple images is 81.66%. The grading errors result from misdetection of stem and errors in defect detection.
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