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Volume 21
Issue 9
10.3390/s21092940
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Communication

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

by
Luciano Ortenzi
1,
Simone Figorilli
1,
Corrado Costa
1,
Federico Pallottino
1,
Simona Violino
1,
Mauro Pagano
1,
Giancarlo Imperi
1,
Rossella Manganiello
1,
Barbara Lanza
2 and
Francesca Antonucci
1,*
1
Consiglio per la Ricerca in Agricoltura e L’Analisi Dell’Economia Agraria (CREA), Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via Della Pascolare 16, 00015 Monterotondo, Rome, Italy
2
Consiglio per la Ricerca in Agricoltura e L’Analisi Dell’Economia Agraria (CREA), Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Viale Lombardia C.da Bucceri, 65012 Cepagatti, Pescara, Italy
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(9), 2940; https://doi.org/10.3390/s21092940
Submission received: 4 March 2021 / Revised: 19 April 2021 / Accepted: 21 April 2021 / Published: 22 April 2021
(This article belongs to the Section Intelligent Sensors)
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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.
Keywords: k-NN; olive maturation index; image analysis; olive harvesting time; image color calibration k-NN; olive maturation index; image analysis; olive harvesting time; image color calibration

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MDPI and ACS Style

Ortenzi, L.; Figorilli, S.; Costa, C.; Pallottino, F.; Violino, S.; Pagano, M.; Imperi, G.; Manganiello, R.; Lanza, B.; Antonucci, F. A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots. Sensors 2021, 21, 2940. https://doi.org/10.3390/s21092940

AMA Style

Ortenzi L, Figorilli S, Costa C, Pallottino F, Violino S, Pagano M, Imperi G, Manganiello R, Lanza B, Antonucci F. A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots. Sensors. 2021; 21(9):2940. https://doi.org/10.3390/s21092940

Chicago/Turabian Style

Ortenzi, Luciano, Simone Figorilli, Corrado Costa, Federico Pallottino, Simona Violino, Mauro Pagano, Giancarlo Imperi, Rossella Manganiello, Barbara Lanza, and Francesca Antonucci. 2021. "A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots" Sensors 21, no. 9: 2940. https://doi.org/10.3390/s21092940

APA Style

Ortenzi, L., Figorilli, S., Costa, C., Pallottino, F., Violino, S., Pagano, M., Imperi, G., Manganiello, R., Lanza, B., & Antonucci, F. (2021). A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots. Sensors, 21(9), 2940. https://doi.org/10.3390/s21092940

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