Automatic Classification of Fruit Defects based on Co-Occurrence Matrix and Neural Networks

Giacomo Capizzi, Grazia Lo Sciuto, Christian Napoli, Emiliano Tramontana, Marcin Woźniak

DOI: http://dx.doi.org/10.15439/2015F258

Citation: Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 5, pages 861–867 (2015)

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Abstract. Nowadays the effective and fast detection of fruit defects is one of the main concerns for fruit selling companies. This paper presents a new approach that classifies fruit surface defects in color and texture using Radial Basis Probabilistic Neural Networks (RBPNN). The texture and gray features of defect area are extracted by computing a gray level co-occurrence matrix and then defect areas are classified by the applied RBPNN solution.