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In this work, a simple vision algorithm is designed and implemented toextract and identify the surface defects on the Golden Delicious apples causedby the enzymic browning process. 34 Golden Delicious apples were selected forthe experiments, of which 17 had enzymic browning defects and the other 17 weresound. The image processing part of the proposed vision algorithm extracted thedefective surface area of the apples with high accuracy of 97.15%. The area andmean of the segmented images were selected as the 2x1 feature vectors to feedinto a designed artificial neural network. The analysis based on the abovefeatures indicated that the images with a mean less than 0.0065 did not belongto the defective apples; rather, they were extracted as part of the calyx andstem of the healthy apples. The classification accuracy of the neural networkapplied in this study was 99.19%

A New Simple Vision Algorithm for Detecting the Enzymic Browning Defects in Golden Delicious Apples