Abstract

Quality rating is currently accomplished by non-destructive and subjective sensory evaluation or by objectiveand destructive analytical techniques. There is a strong need of an objective non-destructive contactless qualityevaluation system to monitor fruit and vegetable along the whole supply chain. This paper proposes a Computervision system to satisfy this request. Image processing and machine learning techniques have been combined todevelop a Computer vision system whose configuration and tuning has been strongly simplified: that makes easierits deployment in real applications. The system has been verified on two white table grape cultivars (Italiaand Victoria) against three different classification tasks. The first considered five quality levels (5, 4, 3, 2, 1); thesecond separated the higher fully marketable quality levels (5 and 4) from the boundary (3) and the waste (2and 1); the third separated the higher fully marketable quality levels (5 and 4) from the other three (3, 2 and1). The system achieved a cross-validation classification accuracy up to 92% on the cultivar Victoria and up to100% on the cultivar Italia for binary or binomial classification between fully marketable and residual qualitylevels. The obtained results support its capability of powerfully, flexibly and continuously monitoring the qualityof the complete production along the whole supply chain

Autore Pugliese

• Pace Bernardo , Attolico Giovanni

Tutti gli autori

• Cavallo D.P.; Cefola M.; Pace B.; Logrieco A.F.; Attolico G.

Titolo volume/Rivista

Computers and electronics in agriculture

Anno di pubblicazione

2018

ISSN

0168-1699

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

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Numero di citazioni Scopus

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Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

Non Disponibile