Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloNon-destructive automatic quality evaluation of fresh-cut iceberg lettuce through packaging material
Anno di pubblicazione2017
FormatoElettronico
Autore/iCavallo D. P.; Cefola M.; Pace B.; Logrieco A. F.; Attolico G.
Affiliazioni autoriInstitute on Intelligent Systems for Automation, CNR-National Research Council of Italy Via G. Amendola, 122/O - 70126 Bari, Italy; Institute of Sciences of Food Production, CNR-National Research Council of Italy Via G. Amendola, 122/O - 70126 Bari, Italy; Institute of Sciences of Food Production, CNR-National Research Council of Italy, URT c/o CS-DAT, Trav. Viale Fortore, 71121 Foggia, Italy; Institute of Sciences of Food Production, CNR-National Research Council of Italy, URT c/o CS-DAT, Trav. Viale Fortore, 71121 Foggia, Italy; Institute on Intelligent Systems for Automation, CNR-National Research Council of Italy Via G. Amendola, 122/O - 70126 Bari, Italy;
Autori CNR e affiliazioni
  • MARIA CEFOLA
  • BERNARDO PACE
  • DARIO PIETRO CAVALLO
  • GIOVANNI ATTOLICO
  • ANTONIO FRANCESCO LOGRIECO
Lingua/e
  • inglese medio (1100-1500)
AbstractNon-destructive evaluation of vegetables by Computer Vision Systems (CVSs) makes possible to check their quality level in an objective and consistent way along the whole supply chain up to the final users. CVSs have been proven to be successful when applied to unpackaged products. The proposed approach aimed to enable this analysis on packaged fresh-cut lettuce with minimum constraints on the acquisition phase and without any care to flatten the surface of the bag facing the camera. A deep-learning architecture, based on Convolutional Neural Networks (CNNs), was used to identify regions of the image where the vegetable was visible with minimum colour distortions due to packaging. To meaningfully assess the performance of the system, each lettuce's sample was acquired both through packaging material and without packaging material. The image analysis was applied to both the resulting images to automatically grade their quality level. The results showed that the performance loss due to the presence of packaging is negligible (83% instead of 86%) and that the proposed system can be used to monitor the quality level of fresh-cut lettuce regardless of packaging at all the critical check points along the supply chain.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da46
Pagine a52
Pagine totali7
RivistaJournal of food engineering
Attiva dal 1981
Editore: Applied Science Publishers. - London
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 0260-8774
Titolo chiave: Journal of food engineering
Titolo abbreviato: J. food eng.
Numero volume della rivista223
Fascicolo della rivista-
DOI10.1016/j.jfoodeng.2017.11.042
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)-
Parole chiaveNon-destructive quality evaluation;, Automatic visual grading through packaging, Deep learning, Convolutional Neural Network
Link (URL, URI)http://www.sciencedirect.com/science/article/pii/S0260877417305174
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioniAvailable online 1 December 2017
Strutture CNR
  • ISPA — Istituto di scienze delle produzioni alimentari
  • STIIMA — Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato
Moduli/Attività/Sottoprogetti CNR
  • AG.P04.008.001 : Sistemi produttivi sostenibili e qualità dei prodotti vegetali
  • DBA.AD004.071.001 : CONTINNOVA Container isotermico intermodale equipaggiato con atmosfera controllata per il trasporto di prodotti ortofrutticoli freschi
Progetti Europei-
Allegati
Non-destructive automatic quality evaluation of fresh-cut iceberg lettuce through packaging material (documento privato )
Descrizione: Published Versione Permessi: Riservato
Tipo documento: application/pdf