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 consisting of a digital camera inside an inspection chamber under a controlled illumination. Images were then, pre-treated for white balance as well as chromatic correction using a profile specifically created 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 colour 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* and CIE b* colour coordinates, green area (%) and veraison area (%) were statistically analysed using ANOVA and PCA. Image analysis results show highly significant differences between the two studied cultivars as well as between the ripening classes. Moreover, PCA results illustrate that, for both cultivars, the main variability is expressed according to the first two components, with a different effect of colour coordinates on these latter.

Assessment of the Ripening of Olives Using Computer Vision

Bernardi B
;
ZIMBALATTI, Giuseppe
2017

Abstract

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 consisting of a digital camera inside an inspection chamber under a controlled illumination. Images were then, pre-treated for white balance as well as chromatic correction using a profile specifically created 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 colour 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* and CIE b* colour coordinates, green area (%) and veraison area (%) were statistically analysed using ANOVA and PCA. Image analysis results show highly significant differences between the two studied cultivars as well as between the ripening classes. Moreover, PCA results illustrate that, for both cultivars, the main variability is expressed according to the first two components, with a different effect of colour coordinates on these latter.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/1472
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