Assessment of the Ripening of Olives Using Computer Vision
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2017Cita bibliográfica
Benalia, 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.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.