Automated apple stem end and calyx detection using evolution-constructed features

Distinguishing stem end and calyx from true defects is the main challenge for an automatic apple blemish detection systems in real time. This paper presents the result of using newly developed Evolutionary COnstructed (ECO) features for distinguishing bruises and blemishes from the stem end and calyx of apple images acquired in near-infrared spectrum. 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 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 Gala apple, a very challenging case, is used in this paper as an example to prove its feasibility, this method could be easily adapted for other stone fruits.