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     Distinguishing stem end and calyx from true defects is the main challenge for automatic apple blemish detection systems. This paper presents the results of using our novel general object recognition algorithm called Evolutionary COnstructed (ECO) features for distinguishing bruises and blemishes from the stem end and calyx of apples. Rather than relying on human experts to build features sets to tune their parameters, our method uses simulated evolution to construct series of transforms that convert the input signal of raw pixels of apple images into high quality features. The use of this method demonstrates the feasibility of using machine vision technology with the off-the-shelf optical and electronics components to detect true bruises and blemishes on apples with higher than 94% accuracy. Although we use apple in this paper as an example to prove its feasibility, this method could be easily adapted for other stone fruits.

Collaborators: Dr. Dong Zhang, Sun Yat-sen University, Guangzhou, China

Graduate Students:

Kirt Lillywhite and Beau Tippetts

Publications:
  1. D. Zhang, K.D. Lillywhite, D.J. Lee, and B.J. Tippetts, “Automated Apple Stem End and Calyx Detection using Evolution-COnstructed Features, “ Journal of Food Engineering, vol. 119/3, p. 411-418, December 2013.
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