Abstract

In green leafy vegetables, the retention of green colour is one of the most generally used index to evaluatethe overall quality and freshness and it is associated to total chlorophyll content.Destructive chemical techniques and non-destructive chlorophyll meters represent the state-of-the-artmethods to accomplish such critical task. The former are effective and robust but also expensive and timeconsuming. The latter are cheaper and faster but exhibit lower reliability, require the probe to touch theleaves and heavily depend on the positions chosen for sampling the leaf's surface. In this paper, a newapproach to non-destructively predict total chlorophyll content of fresh-cut rocket leaves without contactis proposed. Fresh-cut rocket leaves were analysed for total chlorophyll content by spectrophotometerand SPAD-502 (used as reference values) and acquired by a computer vision system using a machinelearningmodel (Random Forest Regression) to predict total chlorophyll content. Finally, the trainedand validated model will be used for on-line prediction of total chlorophyll content of unseen freshcutrocket leaves. The proposed system can match the physical and timing constraints of a real industrialproduction line and its performance (R2 = 0.90), measured on the case study of fresh-cut rocket leaves,outperformed the results of the SPAD chlorophyll meter (R2 = 0.79).

Autore Pugliese

• Logrieco Antonio Francesco , Pace Bernardo , Cefola Maria , 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

2017

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