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Advisor(s)
Abstract(s)
One of the most important matters in international trades for many local apple industries and auctions is accurate fruit quality classification. Defect recognition is a key in online computer-assisted apple sorting machines. Because of the cavity structure of the stem and calyx regions, the system tends to mistakenly treat them as true defects. Furthermore, there is no small-scale sorting machine with a smart vision system for apple quality classification where it is needed. 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. To find an appropriate mode, the effects of different numbers of k and metric distances on stem and calyx region detection were evaluated. Results showed the effectiveness of the value of k and Euclidean distances in recognition accuracy. 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. The significance of the result is very promising in fabricating an advanced small-scale and low-cost sorting machine with a high accuracy for the horticultural industry.
Description
Keywords
Apple sorting Bruise Classification Computer vision k-NN classifier
Pedagogical Context
Citation
Baneh, Nesar Mohammadi; Navid, Hossein; Kafashan, Jalal; Fouladi, Hatef; Gonzales-Barron, Ursula (2023). Development and evaluation of a small-scale apple sorting machine equipped with a smart vision system. AgriEngineering. eISSN 2624-7402. 5:1, p. 1-15
Publisher
MDPI