The application of machine vision in agriculture has increased considerably in recent years. There are many fields in which computer vision is involved, including terrestrial and aerial mapping of natural resources, crop monitoring, precision agriculture, robotics, automatic guidance, non-destructive inspection of product properties, quality control and classification on processing lines and, in general, process automation. This wide range of applications is a result of the fact that machine vision systems provide substantial amounts of information about the nature and attributes of the objects present in a scene. One field where the use of this technology has spread rapidly is the inspection of agri-food commodities and particularly the automatic inspection of fruits and vegetables (Cubero et al., 2010), since it is more reliable and objective than human inspection. The quality of a particular fruit or vegetable is defined by a series of physicochemical characteristics which make it more or less attractive to the consumer, such as its ripeness, size, weight, shape, colour, the presence of blemishes and diseases, the presence or absence of fruit stems, the presence of seeds, its sugar content, and so forth. These characteristics cover all of the factors that exert an influence on the product’s appearance, on its nutritional and organoleptic qualities or on its suitability for preservation. Most of these factors have traditionally been assessed by visual inspection performed by trained operators, but nowadays many of them are estimated with commercial vision systems (Sun, 2007).