In this paper, a system for quality control in citrus fruits is presented. In current citrus manufacturing industries, calliper and color are successfully used for the automatic classification of fruits using vision systems. However, the detection of flaws in the citrus surface is carried out by means of human inspection. In this work, a computer vision system capable of detecting defects in the citrus peel and also classifying the type of flaw is presented. First, a review of citrus illnesses has been carried out in order to build a database of digitalized oranges classified by the kind of fault, which is used as a training set. The segmentation of faulty zones is performed by applying the Sobel gradient to the image. Afterwards, color and texture features of the flaw are extracted considering different color spaces, some of them related to high order statistics. Several techniques have been employed for classification purposes: Euler distance to a prototype, to the nearest neighbor and k-nearest neighbors. Additionally, a three layer neural network has been tested and compared, obtaining promising results.
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