{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T21:06:40Z","timestamp":1774645600235,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T00:00:00Z","timestamp":1619049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005401","name":"Ministero delle Politiche Agricole Alimentari e Forestali","doi-asserted-by":"publisher","award":["DEAOLIVA, D.M. n. 93882\/2017"],"award-info":[{"award-number":["DEAOLIVA, D.M. n. 93882\/2017"]}],"id":[{"id":"10.13039\/501100005401","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005401","name":"Ministero delle Politiche Agricole Alimentari e Forestali","doi-asserted-by":"publisher","award":["INNOLITEC, D.M. 37067\/2018"],"award-info":[{"award-number":["INNOLITEC, D.M. 37067\/2018"]}],"id":[{"id":"10.13039\/501100005401","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 Ja\u00e9n 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\u2014an 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.<\/jats:p>","DOI":"10.3390\/s21092940","type":"journal-article","created":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T21:25:56Z","timestamp":1619126756000},"page":"2940","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1245-8882","authenticated-orcid":false,"given":"Luciano","family":"Ortenzi","sequence":"first","affiliation":[{"name":"Consiglio per la Ricerca in Agricoltura e L\u2019Analisi Dell\u2019Economia Agraria (CREA), Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via Della Pascolare 16, 00015 Monterotondo, Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4035-4199","authenticated-orcid":false,"given":"Simone","family":"Figorilli","sequence":"additional","affiliation":[{"name":"Consiglio per la Ricerca in Agricoltura e L\u2019Analisi Dell\u2019Economia Agraria (CREA), Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via Della Pascolare 16, 00015 Monterotondo, Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3711-1399","authenticated-orcid":false,"given":"Corrado","family":"Costa","sequence":"additional","affiliation":[{"name":"Consiglio per la Ricerca in Agricoltura e L\u2019Analisi Dell\u2019Economia Agraria (CREA), Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via Della Pascolare 16, 00015 Monterotondo, Rome, 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Dell\u2019Economia Agraria (CREA), Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via Della Pascolare 16, 00015 Monterotondo, Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giancarlo","family":"Imperi","sequence":"additional","affiliation":[{"name":"Consiglio per la Ricerca in Agricoltura e L\u2019Analisi Dell\u2019Economia Agraria (CREA), Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via Della Pascolare 16, 00015 Monterotondo, Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5226-5567","authenticated-orcid":false,"given":"Rossella","family":"Manganiello","sequence":"additional","affiliation":[{"name":"Consiglio per la Ricerca in Agricoltura e L\u2019Analisi Dell\u2019Economia Agraria (CREA), Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via Della Pascolare 16, 00015 Monterotondo, Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9588-793X","authenticated-orcid":false,"given":"Barbara","family":"Lanza","sequence":"additional","affiliation":[{"name":"Consiglio per la Ricerca in Agricoltura e L\u2019Analisi Dell\u2019Economia Agraria (CREA), Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Viale Lombardia C.da Bucceri, 65012 Cepagatti, Pescara, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8584-6018","authenticated-orcid":false,"given":"Francesca","family":"Antonucci","sequence":"additional","affiliation":[{"name":"Consiglio per la Ricerca in Agricoltura e L\u2019Analisi Dell\u2019Economia Agraria (CREA), Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via Della Pascolare 16, 00015 Monterotondo, Rome, 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Food Res. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s00217-005-0160-5","article-title":"Changes in quality and phenolic compounds of virgin olive oils during objectively described fruit maturation","volume":"223","author":"Yousfi","year":"2006","journal-title":"Eur. Food Res. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3516","DOI":"10.1021\/jf950585u","article-title":"Influence of fruit ripening on olive oil quality","volume":"44","author":"Garcia","year":"1996","journal-title":"J. Agric. Food Chem."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kailis, S., and Harris, D.J. (2007). Producing Table Olives, Landlinks Press.","DOI":"10.1071\/9780643094383"},{"key":"ref_5","unstructured":"Barranco, D., Fern\u00e1ndez-Escobar, R., and Rallo, L. (2004). El Cultivo del Olivo, Ediciones Mundi-Presna. [5th ed.]."},{"key":"ref_6","unstructured":"Uceda, M., and Frias, L. (1975, January 6\u201317). Harvest dates. Evolution of the fruit oil content, oil composition and oil quality. Proceedings of the II Seminario Oleicola Internacional, International Olive Oil Council, Cordoba, Spain."},{"key":"ref_7","first-page":"355","article-title":"Assessment of the ripening of olives using computer vision","volume":"58","author":"Benalia","year":"2017","journal-title":"Chem. Eng. Trans."},{"key":"ref_8","unstructured":"COI (International Olive Council) (2020, December 16). Guide for the Determination of the Characteristics of Oil Olives, COI\/OH\/Doc. No 1 November 2011. Available online: http:\/\/www.internationaloliveoil.org."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.1007\/s13197-013-1123-7","article-title":"Determination of the olive maturity index of intact fruits using image analysis","volume":"52","author":"Baeten","year":"2015","journal-title":"J. Food Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/S0260-8774(03)00191-2","article-title":"Comparison of three algorithms in the classification of table olives by means of computer vision","volume":"61","author":"Diaz","year":"2004","journal-title":"J. Food Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.jfoodeng.2010.07.016","article-title":"ANN-based method for olive Ripening Index automatic prediction","volume":"101","author":"Furferi","year":"2010","journal-title":"J. Food Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Morene, O.C., Gila, D.M., Puerto, D.A., Garc\u00eda, J.G., and Ortega, J.G. (2015, January 16\u201318). Automatic determination of peroxides and acidity of olive oil using machine vision in olive fruits before milling process. Proceedings of the IEEE International Conference on Imaging Systems and Techniques (IST), Macau, China.","DOI":"10.1109\/IST.2015.7294543"},{"key":"ref_13","first-page":"22","article-title":"Machine vision based fruit classification and grading-a review","volume":"170","author":"Naik","year":"2017","journal-title":"Int. J. Comput. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1071\/AN18522","article-title":"Getting value from artificial intelligence in agriculture","volume":"60","author":"Smith","year":"2020","journal-title":"Anim. Prod. Sci."},{"key":"ref_15","first-page":"233","article-title":"Detecting apples in orchards using YOLOv3 and YOLOv5 in general and close-up images","volume":"Volume 12557","author":"Han","year":"2020","journal-title":"Advances in Neural Networks"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4907","DOI":"10.1016\/j.matpr.2020.08.450","article-title":"Design of disease prediction method based on whale optimization employed artificial neural network in tomato fruits","volume":"33","author":"Kumar","year":"2020","journal-title":"Mater. Today Proc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.3390\/s120607063","article-title":"RGB color calibration for quantitative image analysis: The \u201c3D Thin-Plate Spline\u201d warping approach","volume":"12","author":"Menesatti","year":"2012","journal-title":"Sensors"},{"key":"ref_18","first-page":"86","article-title":"Elaboration of table olives","volume":"57","year":"2006","journal-title":"Grasas Y Aceites"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1109\/34.24792","article-title":"Principal warps: Thin-plate splines and the decomposition of deformations","volume":"11","author":"Bookstein","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pallottino, F., Menesatti, P., Figorilli, S., Antonucci, F., Tomasone, R., Colantoni, A., and Costa, C. (2018). Machine vision retrofit system for mechanical weed control in precision agriculture applications. Sustainability, 10.","DOI":"10.3390\/su10072209"},{"key":"ref_21","unstructured":"Fix, E. (1985). Discriminatory Analysis: Nonparametric Discrimination, Consistency Properties, USAF School of Aviation Medicine."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.scienta.2010.11.008","article-title":"Influence of time of harvest and maturity index on olive oil yield and quality","volume":"127","author":"Dag","year":"2011","journal-title":"Sci. Hortic."},{"key":"ref_24","unstructured":"Barranco, D., Fern\u00e0ndez-Escobar, R., and Rallo, L. (1998). La calidad del aceite de oliva. El cultivo del Olivo, Junta de Andaluc\u00eda."},{"key":"ref_25","unstructured":"Licausi, E., Rotondi, A., and Magli, M. (2010, January 5). Monitoring of atmospheric temperatures in Emilia Romagna region, a new approach to estimate the Jaen Index. Proceedings of the ISHS Acta Hortic, XXVIII International Horticultural Congress on Science and Horticulture for People (IHC2010): Olive Trends Symposium, Lisbon, Portugal."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s11947-009-0211-1","article-title":"Image analysis techniques for automated hazelnut peeling determination","volume":"3","author":"Pallottino","year":"2010","journal-title":"Food Bioproc. Tech."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s11947-008-0106-6","article-title":"Color of salmon fillets by computer vision and sensory panel","volume":"3","author":"Quevedo","year":"2010","journal-title":"Food Bioproc. Tech."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1016\/j.talanta.2013.07.081","article-title":"Infrared machine vision system for the automatic detection of olive fruit quality","volume":"116","author":"Baeten","year":"2013","journal-title":"Talanta"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1016\/j.foodchem.2006.06.012","article-title":"Application of solid-phase microextraction to the analysis of volatile compounds in virgin olive oils from five new cultivars","volume":"102","author":"Baccouri","year":"2007","journal-title":"Food Chem."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/S0308-8146(00)00276-4","article-title":"Influence of fruit ripening on \u2018Cornicabra\u2019 virgin olive oil quality a study of four successive crop seasons","volume":"73","author":"Salvador","year":"2001","journal-title":"Food Chem."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3","DOI":"10.3390\/plants7010003","article-title":"Advances in Non-Destructive Early Assessment of Fruit Ripeness towards Defining Optimal Time of Harvest and Yield Prediction\u2014A Review","volume":"7","author":"Li","year":"2018","journal-title":"Plants"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.compag.2018.01.011","article-title":"A methodology for fresh tomato maturity detection using computer vision","volume":"146","author":"Wan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","first-page":"28","article-title":"A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing","volume":"2","author":"Ireri","year":"2019","journal-title":"Artif. Intell. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kuznetsova, A., Maleva, T., and Soloviev, V. (2020). Using YOLOv3 algorithm with pre-and post-processing for apple detection in fruit-harvesting robot. Agronomy, 10.","DOI":"10.3390\/agronomy10071016"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/9\/2940\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:51:21Z","timestamp":1760161881000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/9\/2940"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,22]]},"references-count":34,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["s21092940"],"URL":"https:\/\/doi.org\/10.3390\/s21092940","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,22]]}}}