It is found that the 5-nearest neighbor classifier and the Euclidean distance using 80 training samples produced the best accuracy rates, at 100% for stem and 97.5% for calyx. Results showed the effectiveness of the value of k and Euclidean distances in recognition accuracy. To find an appropriate mode, the effects of different numbers of k and metric distances on stem and calyx region detection were evaluated. Thus, the current study focuses on a highly accurate and feasible methodology for stem and calyx recognition based on Niblack thresholding and a machine learning technique using k-nearest neighbor (k-NN) classifiers associated with a locally designed small-scale apple sorting machine. Furthermore, there is no small-scale sorting machine with a smart vision system for apple quality classification where it is needed. Because of the cavity structure of the stem and calyx regions, the system tends to mistakenly treat them as true defects. Defect recognition is a key in online computer-assisted apple sorting machines. One of the most important matters in international trades for many local apple industries and auctions is accurate fruit quality classification.
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