Czech J. Food Sci., 2019, 37(2):135-140 | DOI: 10.17221/427/2017-CJFS

Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage processFood Technology and Economy, Engineering and Physical Properties

Krzysztof Przybył2, Piotr Boniecki1, Krzysztof Koszela*,1, Łukasz Gierz3, Mateusz Łukomski1
1 Institute of Biosystems Engineering, Poznan University of Life Sciences, Poznan, Poland
2 Institute of Food Technology and Plant Origin, Poznan University of Life Sciences, Poznan, Poland
3 Faculty of Machines and Transport, Poznan University of Technology, Poznan, Poland

The research methodology consists of several stages to develop a noninvasive method of identifying the turgor of potato tubers during the storage. During the first stage, a graphic database (set of training data) has been created for selected varieties of potatoes. As a next step, special proprietary software called 'PID system' was used together with a commercial MATLAB package to extract parameters defining the digital image descriptors. This included: hue space models, shape coefficient and image texture. Thirdly, Artificial Neural Network (ANN) training was conducted with the use of Statistica and MATLAB tools. As a result of the analysis, a neural model has been obtained, which had the greatest classification features.

Keywords: artificial neural networks; Haralick's texture analysis; image analysis; storage of potatoes

Published: April 30, 2019  Show citation

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Przybył K, Boniecki P, Koszela K, Gierz Ł, Łukomski M. Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process. Czech J. Food Sci.. 2019;37(2):135-140. doi: 10.17221/427/2017-CJFS.
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