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16 April 2014 Organoleptic damage classification of potatoes with the use of image analysis in production process
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Proceedings Volume 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014); 91590W (2014)
Event: Sixth International Conference on Digital Image Processing, 2014, Athens, Greece
In the agro-food sector security it is required the safety of a healthy food. Therefore, the farms are inspected by the quality standards of production in all sectors of production. Farms must meet the requirements dictated by the legal regulations in force in the European Union. Currently, manufacturers are seeking to make their food products have become unbeatable.

This gives you the chance to form their own brand on the market. In addition, they use technologies that can increase the scale of production. Moreover, in the manufacturing process they tend to maintain a high level of quality of their products.

Potatoes may be included in this group of agricultural products. Potatoes have become one of the major and popular edible plants. Globally, potatoes are used for consumption at 60%, Poland 40%. This is due to primarily advantages, consumer and nutritional qualities. Potatoes are easy to digest. Medium sized potato bigger than 60 mm in diameter contains only about 60 calories and very little fat. Moreover, it is the source of many vitamins such as vitamin C, vitamin B1, vitamin B2, vitamin E, etc. [1]. The parameters of quality consumer form, called organoleptic sensory properties, are evaluated by means of sensory organs by using the point method. The most important are: flavor, flesh color, darkening of the tuber flesh when raw and after cooking.

In the production process it is important to adequate, relevant and accurate preparing potatoes for use and sale. Evaluation of the quality of potatoes is determined on the basis of organoleptic quality standards for potatoes. Therefore, there is a need to automate this process. To do this, use the appropriate tools, image analysis and classification models using artificial neural networks that will help assess the quality of potatoes [2, 3, 4].
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
K. Przybył, M. Zaborowicz, K. Koszela, P. Boniecki, W. Mueller, B. Raba, and A. Lewicki "Organoleptic damage classification of potatoes with the use of image analysis in production process", Proc. SPIE 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014), 91590W (16 April 2014);

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