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)
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.