SOFM-type artificial neural network for the non-parametric quality-based classification of potatoes

P. Boniecki; J. Przybył; M. Zaborowicz; K. Górna; J. Dach; P. Okoń; K. Przybył; N. Mioduszewska; P. Idziaszek

doi:10.1117/12.2243907

29 August 2016 SOFM-type artificial neural network for the non-parametric quality-based classification of potatoes

P. Boniecki, J. Przybył, M. Zaborowicz, K. Górna, J. Dach, P. Okoń, K. Przybył, N. Mioduszewska, P. Idziaszek

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100332F (2016) https://doi.org/10.1117/12.2243907
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

Abstract

The classification properties of artificial neural networks, i.e. Self-Organizing Feature Map (SOFM), has been used for the qualitative identification of five varieties of potatoes popular in Poland. The research was based on empirical data obtained in the form of digital images of potatoes, generated at various production phases. They serve to generate a “non-model” SOFM typology map that present the centers of classification example clusters. The radial neurons constituting the structure of the generated typological map were given suitable labels representing the individual varieties. This created the opportunity to build a neural separator to effectively classify the chosen varieties of potatoes produced in Poland.

Citation Download Citation

P. Boniecki, J. Przybył, M. Zaborowicz, K. Górna, J. Dach, P. Okoń, K. Przybył, N. Mioduszewska, and P. Idziaszek "SOFM-type artificial neural network for the non-parametric quality-based classification of potatoes", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100332F (29 August 2016); https://doi.org/10.1117/12.2243907

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