The evolution of olive oil technology is related to research raised to deepen the understanding of biological and biophysical phenomena during the machining process olives, thus allowing the introduction of sensors able to monitoring the parameters and the process according to the characteristics of olives themselves. Current research has identified manufacturing protocols that can enable the achievement levels of product quality required by the market, mainly by reducing the time between collection and processing of olives and raising the technological level of extraction lines. In this context, the present paper aims to describe some of the results of a three-years project developed by the PIN-University of Florence (Italy) in collaboration with the Tuscan Regional Agricultural Development, the Florence Commerce Chamber “Laboratorio Chimico Merceologico—Azienda Speciale CCIAA di Firenze” and the SME “TEM” (Toscana Enologica Mori). The paper will describe the development of an innovative Machine Vision system able to 1) acquire, in real-time, images of olives in the conferring phase and 2) to process the acquired images in order to evaluate the ripeness of olives on the basis of their colour and the eventual presence of superficial defects. The devised system has been tested with the data extracted by olives in the harvesting period of year 2006.
A Machine Vision system for real-time and automatic assessment of olives colour and surface defects / M.Carfagni; R.Furferi. - In: INTERNATIONAL JOURNAL OF COMPUTER RESEARCH. - ISSN 1535-6698. - STAMPA. - 17 Issue 3:(2009), pp. 303-320.
A Machine Vision system for real-time and automatic assessment of olives colour and surface defects
CARFAGNI, MONICA;FURFERI, ROCCO
2009
Abstract
The evolution of olive oil technology is related to research raised to deepen the understanding of biological and biophysical phenomena during the machining process olives, thus allowing the introduction of sensors able to monitoring the parameters and the process according to the characteristics of olives themselves. Current research has identified manufacturing protocols that can enable the achievement levels of product quality required by the market, mainly by reducing the time between collection and processing of olives and raising the technological level of extraction lines. In this context, the present paper aims to describe some of the results of a three-years project developed by the PIN-University of Florence (Italy) in collaboration with the Tuscan Regional Agricultural Development, the Florence Commerce Chamber “Laboratorio Chimico Merceologico—Azienda Speciale CCIAA di Firenze” and the SME “TEM” (Toscana Enologica Mori). The paper will describe the development of an innovative Machine Vision system able to 1) acquire, in real-time, images of olives in the conferring phase and 2) to process the acquired images in order to evaluate the ripeness of olives on the basis of their colour and the eventual presence of superficial defects. The devised system has been tested with the data extracted by olives in the harvesting period of year 2006.File | Dimensione | Formato | |
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International Journal of Computer Research (2009).pdf
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