Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/49835
DC FieldValueLanguage
dc.contributor.authorSalvucci, Giorgiait
dc.contributor.authorPallottino, Federicoit
dc.contributor.authorDe Laurentiis, Leonardoit
dc.contributor.authorDel Frate, Fabioit
dc.contributor.authorManganiello, Rossellait
dc.contributor.authorTocci, Francescoit
dc.contributor.authorVasta, Simoneit
dc.contributor.authorFigorilli, Simoneit
dc.contributor.authorBassotti, Beatriceit
dc.contributor.authorViolino, Simonait
dc.contributor.authorOrtenzi, Lucianoit
dc.contributor.authorAntonucci, Francescait
dc.date.accessioned2023-05-26T09:02:20Z-
dc.date.available2023-05-26T09:02:20Z-
dc.date.issued2022it
dc.identifier.issn1438-2377it
dc.identifier.urihttp://hdl.handle.net/2067/49835-
dc.description.abstractThe presence of olive external damages influences consumers perception in the case of table olive, lowering consumers acceptance and willingness to purchase. Defects cause a decrease of extra virgin olive oil quality and its shelf-life. Indeed, fruit external quality represents an important factor for marketing and oil quality characteristics. In this context, RGB image processing systems can potentially support the production of high-quality products through the automatic and rapid classification into different qualitative classes of both lots for oil production and table olives. The neural networks known as Deep Neural Networks represent a kind of artificial intelligence that demonstrated very high levels of accuracy in different application fields. The aim of the present study regards the rapid classification through RGB images and advanced Convolutional Neural Network modeling (YOLO, You Only Look Once) for olives selection on the base of defects and color. The model was trained, tested and evaluated for the future realization of an optomechanical RGB sorting system for real-time olive classification into different classes (e.g., ripening, defects, etc.) through the simultaneous extraction of parameters and dedicated features. The images acquisition was carried out with a high-resolution RGB camera equipped on a laboratory conveyor belt. The algorithm was trained using two datasets: the first made of 1500 oil olive images (i.e., Carboncella, Frantoio and Leccino cultivars), the second one of 930 table olive images (i.e., Bella di Cerignola cultivar). The classification accuracy resulted to be above 95% for both datasets, as required by the high-efficiency standards of a selection prototype.it
dc.format.mediumSTAMPAit
dc.language.isoengit
dc.titleFast olive quality assessment through RGB images and advanced convolutional neural network modelingit
dc.typearticle*
dc.identifier.doi10.1007/s00217-022-03971-7it
dc.identifier.scopus2-s2.0-85125037081it
dc.identifier.isi000756096900001it
dc.identifier.urlhttps://link.springer.com/article/10.1007/s00217-022-03971-7it
dc.relation.journalEUROPEAN FOOD RESEARCH AND TECHNOLOGYit
dc.relation.firstpage1395it
dc.relation.lastpage1405it
dc.relation.projectMIPAAF projects: DEAOLIVA, D.M. n. 93882/2017; INNOLITEC, D.M. 37067/2018it
dc.relation.volume248it
dc.relation.issue5it
dc.subject.scientificsectorINF/01 INFORMATICA, ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONIit
dc.subject.keywordsYou Only Look Once (YOLO) · Deep Neural Networks (DNNs) · Image analysis · Oil and table olives · Artifcial Intelligence (AI)it
dc.subject.ercsectorPE6_7, PE6_8, PE6_11,it
dc.description.numberofauthors12it
dc.description.internationalnoit
dc.description.noteAuthor contributions Methodology: GS, FDF, FA; Software: GS, LDeL, FDelF; Validation: GS, FP, FDelF, FA; Writing-original draft: GS, FDelF, RM, FA; Writing-review and editing: FP, RM, FA; Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision: FP, FA; Visualization: FP, FT, SV, SF, BB, SV, LO, FA; Data curation: FAit
dc.contributor.countryITAit
dc.type.refereeREF_1it
dc.type.miur262*
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.grantfulltextrestricted-
item.openairetypearticle-
crisitem.author.orcid0000-0002-1245-8882-
crisitem.journal.journalissn1438-2377-
crisitem.journal.anceE060561-
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