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

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

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