A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots.

Luciano Ortenzi,Simona Violino,Giancarlo Imperi,Rossella Manganiello,Barbara Lanza,Simone Figorilli,Corrado Costa,Federico Pallottino,Francesca Antonucci,Mauro Pagano

doi:10.3390/s21092940

Luciano Ortenzi, Simona Violino + Show 8 more

Open Access

https://doi.org/10.3390/s21092940

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Abstract

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.

Highlights

Published: 22 April 2021The maturation stage of olives, both for oil and table uses, is one of the most important factors for organoleptic quality to be considered at harvest time [1,2]
Picked olives should be collected into 20–25 kg slotted trays, and picking the fruit at the correct ripeness stage is the most important factor, assuring that only quality fruit is chosen [4]
Through a potentially real-time, repeatable, and objective machine vision method. in this work, in order to overcome the different environmental situations that can occur in dynamic environments, pictures were subjected to an automatic colorimetric calibration method developed by Menesatti et al [17]

Summary

Introduction

The maturation stage of olives, both for oil and table uses, is one of the most important factors for organoleptic quality to be considered at harvest time [1,2]. As reported by Garcia et al [3], olive ripening begins after a period of 25 weeks of cell growth, and during this time the fruit develops to its final size, maintaining the original green skin color. In this first stage (i.e., green stage), olives are most suitable for processing by any method as green table olives. As reported by Benalia et al [7], not all the drupes of an orchard or tree turn color at the same time, so rapidly assessing the maturity index (MI)

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