Stem and Calyx Recognition on ‘Jonagold’ Apples by Pattern Recognition

In this paper, a novel method to recognize stem or calyx regions of ‘Jonagold’ apples by pattern recognition is proposed. The method starts with background removal and object segmentation by thresholding. Statistical, textural and shape features are extracted from each segmented object and these features are introduced to several supervised classification algorithms. Linear discriminant, nearest neighbor, fuzzy nearest neighbor, support vector machines classifiers and adaboost are the ones tested. Relevant features are selected by floating forward feature selection algorithm. Support vector machines, which is found to be the best among all classification algorithms tested, correctly recognized 99 % of the stems and 100 % of the calyxes using selected feature subset. These results exhibit considerable improvement relative to the ones introduced in the literature.