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Quality evaluation of dried carrot obtained in different drying conditions using deep convolutional neural networks


[ 1 ] Instytut Konstrukcji Maszyn, Wydział Inżynierii Mechanicznej, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.9] Mechanical engineering

Year of publication


Chapter type

chapter in monograph / paper

Publication language


  • dried vegetables
  • neural networks
  • dried carrot cubes
  • deep learning process

EN In recent years one can observe a continuous growth of demand for dried vegetables. This tendency has an impact on the development of dehydrated food market segment in Poland, which enables to manage a surplus of vegetable production. More and more often dried vegetables are used in various sectors of food industry, both because of their high nutritional values and because of the changing nutritional habits among customers. Among dried vegetables, dried carrots seem to play a strategic role on account of the fact that this produce has a wide spectrum of applications and is famous for its high nutritional value. The research was conducted in order to evaluate the quality of dried carrot cubes using three different techniques of drying. It should be noted that during the research both correct and incorrect dried carrots were used. What is more, the process of deep learning of convolutional artificial neural networks was carried out with MobileNet architecture for classification, for a selected research sample. The classification included both the type of drying process and the quality of drying for binary division on account of the applied parameters. The obtained models were characterized by high capability to classify samples at the level of 85 - 100% with the exception of lyophilized dried carrots where the trained network reached the effectiveness of classification at the level of 71% for the validation set. The research proved that fast and noninvasive evaluation of the quality of dried carrot cubes in different conditions is possible and highly effective using artificial neural networks.

Pages (from - to)

252 - 259





Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

Presented on

14th International Conference on Digital Image Processing, ICDIP 2022, 20-23.05.2022, Wuhan, China

Ministry points / chapter


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