21 July 2017 Classification of dried vegetables using computer image analysis and artificial neural networks
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 1042031 (2017) https://doi.org/10.1117/12.2281718
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
Abstract
In the recent years, there has been a continuously increasing demand for vegetables and dried vegetables. This trend affects the growth of the dehydration industry in Poland helping to exploit excess production. More and more often dried vegetables are used in various sectors of the food industry, both due to their high nutritional qualities and changes in consumers’ food preferences. As we observe an increase in consumer awareness regarding a healthy lifestyle and a boom in health food, there is also an increase in the consumption of such food, which means that the production and crop area can increase further. Among the dried vegetables, dried carrots play a strategic role due to their wide application range and high nutritional value. They contain high concentrations of carotene and sugar which is present in the form of crystals. Carrots are also the vegetables which are most often subjected to a wide range of dehydration processes; this makes it difficult to perform a reliable qualitative assessment and classification of this dried product. The many qualitative properties of dried carrots determining their positive or negative quality assessment include colour and shape. The aim of the research was to develop and implement the model of a computer system for the recognition and classification of freeze-dried, convection-dried and microwave vacuum dried products using the methods of computer image analysis and artificial neural networks.
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K. Koszela, M. Łukomski, W. Mueller, K. Górna, P. Okoń, P. Boniecki, M. Zaborowicz, D. Wojcieszak, "Classification of dried vegetables using computer image analysis and artificial neural networks", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042031 (21 July 2017); doi: 10.1117/12.2281718; https://doi.org/10.1117/12.2281718
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