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

Content Provider	Springer Nature Link
Author	Riccardi, M. Mele, G. Pulvento, C. Lavini, A. d’Andria, R. Jacobsen, S. E.
Copyright Year	2014
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 lower cost 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.
Starting Page	263
Ending Page	272
Page Count	10
File Format	PDF
ISSN	01668595
Journal	Photosynthesis Research
Volume Number	120
Issue Number	3
e-ISSN	15735079
Language	English
Publisher	Springer Netherlands
Publisher Date	2014-01-19
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Leaf image analysis RGB multi-regression model Chenopodium quinoa Amaranthus sp. Plant Sciences Plant Physiology Biochemistry Plant Genetics & Genomics
Content Type	Text
Resource Type	Article
Subject	Cell Biology Plant Science Medicine Biochemistry

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