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Applying Neural Networks to Automated Visual Fruit Sorting

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Pp. 1-7 in Proceedings of the World Congress of Computers in Agriculture and Natural Resources (13-15, March 2002, Iguacu Falls, Brazil)  701P0301.(doi:10.13031/2013.8303)
Authors:   A. S. Simões, A.H. Reali Costa, A. R. Hirakawa and A. M. Saraiva
Keywords:   machine vision, neural networks, agriculture automation, color inspection

A common problem in fruit production systems is sorting and classification. A usual procedure to carry out this task is based on human visual inspection considering general fruit attributes like color and size. Color contains important information about fruit status and in some cases it is decisive for fruit quality differentiation. An adequate color classification can improve system accuracy and productivity. Large-scale utilization of automatic classification system for this purpose demands a robust color classification even under different color saturation, variations of environment lighting and light reflections. This paper provides an investigation on the applicability of color classification using an artificial neural network in the fruit-sorting domain. Using the well-known network generalization property we investigate the applicability of this approach to the segmentation of colored images represented by the RGB color system. Jointly with color analysis, we also use some shape analysis to generate a robust and real time system that was tested for orange classification according to a Brazilian standard and which was able to provide fruit classification under less restricted visual conditions.

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