Please use this identifier to cite or link to this item:
Title: A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots
Authors: Ortenzi, Luciano 
Figorilli, Simone
Costa, Corrado
Pallottino, Federico
Violino, Simona
Pagano, Mauro
Imperi, Giancarlo
Manganiello, Rossella
Lanza, Barbara
Antonucci, Francesca
Journal: SENSORS 
Issue Date: 2021
The degree of olive maturation is a very important factor to consider at harvest time, as it influences the organoleptic quality of the final product, for both oil and table use. The Jaén index, evaluated by measuring the average coloring of olive fruits (peel and pulp), is currently considered to be one of the most indicative methods to determine the olive ripening stage, but it is a slow assay and its results are not objective. The aim of this work is to identify the ripeness degree of olive lots through a real-time, repeatable, and objective machine vision method, which uses RGB image analysis based on a k-nearest neighbors classification algorithm. To overcome different lighting scenarios, pictures were subjected to an automatic colorimetric calibration method-an advanced 3D algorithm using known values. To check the performance of the automatic machine vision method, a comparison was made with two visual operator image evaluations. For 10 images, the number of black, green, and purple olives was also visually evaluated by these two operators. The accuracy of the method was 60%. The system could be easily implemented in a specific mobile app developed for the automatic assessment of olive ripeness directly in the field, for advanced georeferenced data analysis.
ISSN: 1424-8220
DOI: 10.3390/s21092940
Rights: Attribution 4.0 International
Appears in Collections:A1. Articolo in rivista

Files in This Item:
File Description SizeFormat
sensors-21-02940.pdf1.37 MBAdobe PDFView/Open
Show full item record


Last Week
Last month
checked on Nov 25, 2023

Page view(s)

Last Week
Last month
checked on Nov 29, 2023


checked on Nov 29, 2023

Google ScholarTM



This item is licensed under a Creative Commons License Creative Commons