Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process

Krzysztof Przybył, Piotr Boniecki, Krzysztof Koszela, Łukasz Gierz and Mateusz Łukomski
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Krzysztof Przybył: Institute of Food Technology and Plant Origin, Poznan University of Life Sciences, Poznan, Poland
Piotr Boniecki: Institute of Biosystems Engineering, Poznan University of Life Sciences, Poznan, Poland
Krzysztof Koszela: Institute of Biosystems Engineering, Poznan University of Life Sciences, Poznan, Poland
Łukasz Gierz: Faculty of Machines and Transport, Poznan University of Technology, Poznan, Poland
Mateusz Łukomski: Institute of Biosystems Engineering, Poznan University of Life Sciences, Poznan, Poland

Czech Journal of Food Sciences, 2019, vol. 37, issue 2, 135-140

Abstract: 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 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlcjf:v:37:y:2019:i:2:id:427-2017-cjfs

DOI: 10.17221/427/2017-CJFS

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