Assessment of the Ripening of Olives Using Computer Vision
MetadataShow full item record
Cita bibliográficaBenalia, S., Bernardi, B., Blasco, J., Fazari, A., Zimbalatti, G. (2017). Assessment of the ripening of olives using computer vision. Chemical Engineering Transactions, 58, 355-360.
In the framework of a continuously evolving global market, olive and olive oil industries should introduce new and innovative technologies in order to enhance their productivity and improve their competitiveness. Computer vision systems (CVS) employed in automated processes for olive sorting and/or quality inspection constitute a promising tool that allow these industries to respond to the global market requirements. One of the application of CVS in this sector may be the prediction of olive ripening through data obtained from machine vision systems, in order to achieve a proper processing and obtain high quality products. Indeed, either for olive oil or table olives, ripening degree represents a key factor that influences the final product features. In this context, the present study aims to evaluate colour changes during olive ripening using a computer vision system. Experimental trials considered two olive (Olea europaea L.) cultivars, namely, Carolea and Nocellara. First, experienced operators classified the olives visually in five different ripening classes for Carolea and six classes for Nocellara. After that, olive image acquisition was carried out employing a laboratory computer vision system considering the following operative parameters: sensitivity ISO 100, exposition time 1/250 seconds and diaphragm opening f 4.5. Images were then, pre-treated for white balance as well as chromatic correction using a specifically created profile with Colorchecker Passport Software (X-Rite Inc, USA), and subsequently analysed using Food-Color Inspector 3.5 (Cofilab) Software, which allowed obtaining the segmentation models for RGB olive images and the subsequent analysis of their features. The obtained data from image analysis expressed in terms of R, G, B, CIE L*CIE a*CIE b*green area (%) and veraison area (%) were statistically analysed. The results showed that the conventional visual classification of both varieties lead to possible inaccuracies, especially for green olives as well as at early veraison stages. Moreover, image analysis outcomes showed to be more reliable to determine precisely olive colour attributes. Copyright � 2017, AIDIC Servizi S.r.l.