http://www.cnr.it/ontology/cnr/individuo/prodotto/ID276761

Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components (Articolo in rivista)

Type

Articolo in rivista (Classe)
Prodotto della ricerca (Classe)

Label

Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components (Articolo in rivista) (literal)

Anno

2014-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1007/s11120-014-9970-2 (literal)

Alternative label

M. Riccardi 1* o G. Mele 1* o C. Pulvento 1 o A. Lavini 1 o R. d'Andria 1 o S. -E. Jacobsen 2 (2014)
Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components
in Photosynthesis research (Print); Springer Science+Business Media, Dordrecht (Germania)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

M. Riccardi 1* o G. Mele 1* o C. Pulvento 1 o A. Lavini 1 o R. d'Andria 1 o S. -E. Jacobsen 2 (literal)

Pagina inizio

263 (literal)

Pagina fine

272 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni

Research carried out in this paper was part of the projects: EU FP7 SWUP-MED (Sustainable Water Use Securing Food Production in Dry Areas of the Mediterranean Region) and CISIA (Integrate knowledge for Sustainability and Innovation of the Made in Italy Agro-food products). M. Riccardi and G. Mele are the co-first authors (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

http://link.springer.com/article/10.1007/s11120-014-9970-2 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

120 (literal)

Rivista

Photosynthesis research (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

10 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

1-2 (literal)

Note

ISI Web of Science (WOS) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

1 CNR - Institute for Agricultural and Forest Mediterranean Systems (ISAFoM), Ercolano, NA, Italy 2 Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Taastrup, Denmark * co-first author (literal)

Titolo

Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components (literal)

Abstract

Leaf chlorophyll content provides valuable information about physiological status of plants; it is directly linked to photosynthetic potential and primary production. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time consuming. Over the years alternative methods, rapid and non-destructive, have been explored. The aim of this work was to evaluate the applicability of a fast and non-invasive field method for estimation of chlorophyll content in quinoa and amaranth leaves based on RGB components analysis of digital images acquired with a standard SLR camera. Digital images of leaves from different genotypes of quinoa and amaranth were acquired directly in the field. Mean values of each RGB component were evaluated via image analysis software and correlated to leaf chlorophyll provided by standard laboratory procedure. Single and multiple regression models using RGB color components as independent variables have been tested and validated. The performance of the proposed method was compared to that of the widely used non-destructive SPAD method. Sensitivity of the best regression models for different genotypes of quinoa and amaranth was also checked. Color data acquisition of the leaves in the field with a digital camera was quick, more effective, and less costly than SPAD. The proposed RGB models provided better correlation (highest R 2) and prediction (lowest RMSEP) of the true value of foliar chlorophyll content and had a lower amount of noise in the whole range of chlorophyll studied compared with SPAD and other leaf image processing based models when applied to quinoa and amaranth. * M. Riccardi and G. Mele are the co-first authors (literal)

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