Development and Evaluation of a Small-Scale Apple Sorting Machine Equipped with a Smart Vision System

Development and Evaluation of a Small-Scale Apple Sorting Machine Equipped with a Smart Vision System

Blog Article

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 Non-Alcoholic Beverages 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 Dresser 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.