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

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.

Relationship between leaf physiologic traits and canopy color indices during the leaf expansion period in an oak forest

“…The SPAD values are calculated through the amount of light transmitted by the leaf in red light (650 nm) and infrared light (940 nm), which have different absorbance induced by chlorophyll. Although the relationship between SPAD values and chlorophyll contents could be linear, exponential or polynomial correlations for various kinds of plants species have been reported; this relationship could be affected by light conditions, chloroplast distribution or the level of chlorophyll concentrations, but overall, the dynamic curve of SPAD values could be used as the indicator of leaf chlorophyll contents (Markwell et al 1995, v www.esajournals.org Muraoka and Koizumi 2005, Uddling et al 2007, Naus et al 2010, Ling et al 2011, Nagai et al 2011, Djumaeva et al 2012, Scattolin et al 2013, Riccardi et al 2014. Furthermore, the daily leaf area index (LAI) values (lower frequency after the end of leaf expansion) within ROI were collected with the leaf area meter LAI-2200 (Li-COR, Nebraska, USA) at the same time with other measurements.…”

Estimation of Water Stress in Grapevines Using Proximal and Remote Sensing Methods

“…As an example, various imaging methods became common for the analysis of plant stress responses by automated phenotyping [17]. In addition, several imaging techniques have been developed to detect early signs of stress by monitoring changes in water status, photosynthetic efficiency and the accumulation of secondary metabolites or structural modifications [18][19][20][21].…”

Plant Diseases Recognition Based on Image Processing Technology

“…Color cutting is to use a sphere to determine a color of the cluster; extract the following: set the appropriate color clustering sphere radius ( 0 = 70) to filter out useless stains [9]. As shown in Figure 6, the user chooses the certain color parts.…”