Applying neural networks to automated visual fruit sorting

Abstract

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 colour and size. Colour 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 utilisation of an automatic classification system demands a robust colour classification even under different colour saturation, variations of environment lighting and light reflections. This paper investigates the applicability of colour classification using an artificial neural network in the fruit-sorting domain. Using the well-known network generalisation property, we investigate the applicability of this approach to the segmentation of coloured images represented by the RGB colour system. Jointly with colour 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.