  In this work, a simple vision algorithm is designed and implemented to
extract and identify the surface defects on the Golden Delicious apples caused
by the enzymic browning process. 34 Golden Delicious apples were selected for
the experiments, of which 17 had enzymic browning defects and the other 17 were
sound. The image processing part of the proposed vision algorithm extracted the
defective surface area of the apples with high accuracy of 97.15%. The area and
mean of the segmented images were selected as the 2x1 feature vectors to feed
into a designed artificial neural network. The analysis based on the above
features indicated that the images with a mean less than 0.0065 did not belong
to the defective apples; rather, they were extracted as part of the calyx and
stem of the healthy apples. The classification accuracy of the neural network
applied in this study was 99.19%
