RT article
T1 In-line sorting of irregular potatoes by using automated computer-based machine vision system
A1 ElMasry, Gamal
A1 Cubero, Sergio
A1 Moltó, Enrique
A1 Blasco, José
AB This study was conducted to develop a fast and accurate computer-based machine vision system for detecting irregular potatoes in real-time. Supported algorithms were specifically developed and programmed for image acquisition and processing, controlling the whole process, saving the classification results and monitoring the progress of all operations. A database of images was first formulated from potatoes with different shapes and sizes, and then some essential geometrical features such as perimeter, centroid, area, moment of inertia, length and width were extracted from each image. Also, eight shape parameters originated from size features and Fourier transform were calculated for each image in the database. All extracted shape parameters were entered in a stepwise linear discriminant analysis to extract the most important parameters that most characterized the regularity of potatoes. Based on stepwise linear discriminant analysis, two shape features (roundness and extent) and four Fourier-shape descriptors were found to be effective in sorting regular and irregular potatoes. The average correct classification was 96.5% for a training set composed of 228 potatoes and then the algorithm was validated in another testing set composed of 182 potatoes in a real-time operation. The experiments showed that the success of in-line classification of moving potatoes was 96.2%. Concurrently, the well-shaped potatoes were classified by size achieving a 100% accuracy indicating that the developed machine vision system has a great potential in automatic detection and sorting of misshapen products. (c) 2012 Elsevier Ltd. All rights reserved.
SN 0260-8774
YR 2012
FD 2012
LK http://hdl.handle.net/20.500.11939/5159
UL http://hdl.handle.net/20.500.11939/5159
LA en
NO ElMasry, Gamal, Cubero, Sergio, Molto, E., Blasco, J. (2012). In-line sorting of irregular potatoes by using automated computer-based machine vision system. Journal of Food Engineering, 112(1-2), 60-68.
DS MINDS@UW
RD Feb 5, 2023