Abstract
In-line automated inspection of raw materials is one of the major concerns in food industry. The aim of this paper is to devise a method to sort pistachio nuts, in order to reject the bad ones. X-ray images are used to compute a set of fuzzy features, with membership functions automatically inferred from the positive samples. A functional-link neural network is then used for the proper classification task. By means of a repeated cross-validation, the proposed solution showed a correct recognition rate of 99.6%, with a false positive rate of 0.3% with a single classifier and 0.1% with a combined one.
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Ros, P.M., Pasero, E. (2013). Defects Detection in Pistachio Nuts Using Artificial Neural Networks. In: Apolloni, B., Bassis, S., Esposito, A., Morabito, F. (eds) Neural Nets and Surroundings. Smart Innovation, Systems and Technologies, vol 19. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35467-0_16
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DOI: https://doi.org/10.1007/978-3-642-35467-0_16
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