An Approach for Recognizing Stem-end/calyx Regions in Apple Quality Sorting

In this paper we introduce a cascaded-classifier approach to localize stem-ends and calyxes of ‘Jonagold’ apples. First classifier (artificial neural network) extracts candidate objects, whereas the second one (nearest neighbor) discriminates stem-ends and calyxes from others. Overall system is tested by 616 fruits from which first classifier found 414 candidate objects. Several features are extracted from these objects and these features are then selected by forward selection method. With these selected features the second classifier reached to 80% recognition rate. Classification errors of each feature is also introduced for comparison.