Agricultural Science Digest

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Agricultural Science Digest, volume 42 issue 1 (february 2022) : 68-71

A Comparative Study of Chlorophyll Content Estimation in Barley (Hordeum vulgare L.) Genotypes Based on RGB (Red, Green, Blue) Image Analysis

Ali Guendouz1,*, Hocine Bendada2, Ramadan Benniou3
1National Institute of the Agronomic Research of Algeria, Setif, Algeria.
2Technical Institute of Field Crops, Setif, Algeria.
3Department of Agronomy, University of M’sila, Algeria.
Cite article:- Guendouz Ali, Bendada Hocine, Benniou Ramadan (2022). A Comparative Study of Chlorophyll Content Estimation in Barley (Hordeum vulgare L.) Genotypes Based on RGB (Red, Green, Blue) Image Analysis . Agricultural Science Digest. 42(1): 68-71. doi: 10.18805/ag.D-305.
Background: Chlorophyll is the most important pigment in plant which absorbs light and subsequently transfers its energy to drive the photochemical reactions of photosynthesis. The numerical image processing techniques have been widely used in the analysis of leaf characteristics.

Methods: The methods based on RGB (Red, Green and Blue) image analysis may emerge as a new and low-cost method for estimation the chlorophyll content. In this work, we use eight RGB vegetation indices as alternative for chlorophyll content estimation. 

Result: The student t-test showed that all the RGB indices tested are suitable to estimate the chlorophyll content in barley genotypes. In addition, the results which based on the correlation analysis in combination with the values of root mean squared error (RMSE) demonstrate that the very suitable RGB indices are these with high values of correlation coefficient and lowest values of RMSE. Data collected from barley genotypes leaves indicated that digital image processing technology can be a useful and rapid non-destructive method for assessment of chlorophyll content. Among the RGB indexes tested in this study the 100-(2R-B) and RGRI (R/G) are the most promising index to estimate the chlorophyll content in barley genotypes.
The quality and productivity of crop are directly related to the green pigment visible in the leaves which is due to the presence of Chlorophyll. Leaf chlorophyll is mostly used as an index to diagnose diseases and getting the nutrient and nitrogen status in the plants. The chlorophyll content of plants can provide useful information concerning photosynthetic status during plant growth and can be used as a key indicator of the physiological stage of plants. Several methods for estimating chlorophyll content can be found in the current literature and are based on the transmittance or reflectance of the leaf; nevertheless, chloroplast arrangement in the cells is modified by the intensity, color and duration of the incident light, which produces variations in the values obtained with measurement devices (Pérez-Patricio, 2018). Digital cameras can record spectral leaf information in visible color bands, with high resolutions and low costs (Guendouz et al., 2012; Guendouz and Hafsi, 2017; Bama et al., 2011). In addition, digital color images contain rich information of plant morphology, structure and leaf colors. So, leaf digital images are often exploited to identify changes in leaf color (Zhang et al., 2014). The most commonly used color representation for digital color images is the RGB color model. For an RGB color image, three color sensors per pixel can be used to capture the intensity of light in the red, green and blue channels, respectively. Digital cameras or scanners in combination with computers and appropriate software can be used to photograph, scan and evaluate leaves for color with relative ease and at an affordable cost. Many software tools, such as MATLAB and Mesurim Pro are used to process the obtained digital pictures (Chen et al., 2020; Guendouz and Hafsi, 2017). A common non-destructive device is the Minolta SPAD-502 leaf chlorophyll meter. It measures the transmittance of red (650 nm) and infrared (940 nm) radiation through the leaf. The main disadvantage of the SPAD system is that it only estimates the transmittance at one point of the leaf under analysis, calculating the chlorophyll content only within a small spatial location on the leaf. To solve this problem, iterative measurements at different spatial locations must be performed (Pérez-Patricio, 2018). Many studies and researchers suggested a number of RGB-based color features for the determination of chlorophyll levels in potato, rice, wheat, broccoli, cabbage, barley, tomatoes, quinoa and amaranth (Adamsen et al., 1999; Yadav et al., 2010; Guendouz and Hafsi, 2017). Almost all the methods applied so far to determine the amounts of chlorophyll in wheat and Barley from leaf color information are RGB color index based. In addition, Guendouz et al., (2013) proved that the declines in chlorophyll content with time affect the final grain yield in durum wheat. Therefore, to overcome this, a new chlorophyll meter (SPAD 502) is an improved model over SPAD 501 was developed by the Minolta Camera Company, Japan and made commercially available (Pushpanathan et al., 2014). Kawashima and Nakatani (1998) proposed (R-B)/(R+B) as a good tool to estimate wheat chlorophyll content; Guendouz and Hafsi (2017) described the positive and significant linear relationship between 100-(R+B) and 100-(2R-B) and SPAD reading in durum wheat. The objective of this study is to evaluate the efficiency of using the RGB index to estimate chlorophyll content in some genotypes of Barley (Hordeum vulgare L.) growing under semi-arid conditions.
Field experiment was conducted during the 2018-2019 cropping season at the experimental field of ITGC, Setif, Algeria (5°20'E, 36°8'N, 958 m above mean sea level). The statistical design employed was based on a complete randomized block design (CRBD) with three replications. Eight Barley (Hordeum vulgare L.) genotypes were used in this study. The seeds were sown using an experimental drill in 1.2 m × 2.5 m plots consisting of 6 rows with a 20 cm row space and the seeding rate is about 250 seeds per m2. During this study, we use the numerical image analysis (NIA) to estimate the reflectance at Red, Green and Blue (RGB). Leaves were photographed between 11:00 and 13:00 solar time with a color digital camera (Canon, Power Shot A460, AiAF, CHINA). Images were stored in a JPEG (Joint Photographic Expert Group) prior to downloading onto a PC computer and analyzed using Mesurim Pro (Version 3.3) software (Fig 1). The RGB indices used for evaluation are listed and defined in Table 1. The SPAD-502 measures the content of chlorophyll (Chl) in the leaf, which is related to leaf greenness, by transmitting light from light emitting diodes (LED) through a leaf at wavelengths of 650 and 940 nm. Simple linear regression was employed to determine the strength of the relationships between the SPAD measurements and the indices. The root mean squared error (RMSE), coefficient of correlation (r), the coefficient of determination (R²) and the student test (t-test) were used to compare the performance of the RGB indices tested.

Fig 1: Description of measuring the reflectance at RGB (Red, Green, Blue) using Mesurim Pro software (Guendouz et al., 2012).


Table 1: List of RGB indices tested in this study.

We use during this study eight RGB index to estimate flag leaf chlorophyll content in barley genotypes. The models relating between the RGB indices and the measured chlorophyll content by SAPD-502 illustrated in Table 2. In addition, as shown in Table 3, the chlorophyll content measured by SPAD ranged from 33.50 for the genotype Tichedrette to 52.10 for the genotype Rihane 03. The correlation analysis between the chlorophyll measured by SPAD and the chlorophyll estimated by the RGB index models showed no significant correlation between the values estimated based on the Green and Blue models (r = 0.373 and r = 0.12); this results proved that the Green and Blue index are unsuitable to estimate chlorophyll content. Various functions derived from the R, G and B values and the correlation coefficients between the function values and chlorophyll content are shown in Table 3; the estimated chlorophyll content based on the Kawashima and Nakatani (1998) index [R/(R+G+B)] showed a significant and positive correlation with the measured chlorophyll by SPAD (r = 0.872**) but with highest values of RMSE (5.52). The best RGB indexes were selected based on the highest r values and lower values of RMSE. The results obtained for this indices were consistent with the findings of Kawashima and Nakatani (1998). In contrary of our finding, Hu et al., (2010), obtained highest R2 values for regressions between R and G with the total chlorophyll content in leaves of three barley cultivars, but low Pearson correlation coefficients (r) when the indexes R/(R + B + G) is tested. The chlorophyll content estimated based on the RGB index 2R-B and the index proposed by Guendouz and Hafsi (2017) [100-(2R-B)] showed significant and positive correlation between the SPAD reading chlorophyll content with moderate value of RMSE (2.40) for the index 2R-B, but with lowest value of RMSE for the RGB index 100-(2R-B), this results is in agreement with the finding of Guendouz and hafsi (2017). In addition, Moghaddam et al., (2011) showed acceptable correlation between 2R-B function and Chlorophyll content measured by SPAD. The estimated chlorophyll based on the proposed RGB index by Pettorelli et al., (2005) (GRVI) showed significant and positive correlation with the SPAD reading (r = 0.753*) with moderate value of RMSE (4.27). The results show a best correlation between the SPAD reading and the estimated chlorophyll based on the R/G with lowest RMSE (0.072) which suggests that this index it’s very suitable to estimate chlorophyll content. Saberioon et al., (2014) registered no significant correlation between the chlorophyll estimated by R/G (RGRI) index and the chlorophyll measured by SPAD. Overall, based on the t-test (student test) all the RGB indices tested showed no significant difference between the chlorophyll estimated and the SPAD reading (Table 3) which indicate the efficiency of these indices to estimate chlorophyll content.

Table 2: Regression equations, coefficients of determination (R²) for models relating SPAD-502 index (SPAD) with eight RGB indices.


Table 3: Correlation coefficient and models evaluation between chlorophyll content measured by SPAD instrument and chlorophyll content estimated by RGB indices.

Eight RGB vegetation indices were evaluated for chlorophyll content estimation in barley leaves at the heading stage of the cultivation period. Based on correlations analysis of the RGB index determined chlorophyll contents and SPAD reading the more suitable indices are Red, r [R/(R+G+B)], 2R-B, 100-(2R-B), GRVI [(G-R)/(G+R)] and RGRI (R/G) with significant and positive correlation coefficient. In addition, the student test (t-test) proved that the all RGB indices tested are suitable to estimate the chlorophyll content.  In combination between the correlation coefficient and root-mean-square error (RMSE) the very suitable indices are these which have the highest r values and lower values of RMSE; in this case the index 100-(2R-B) and RGRI (R/G) are the very suitable index.

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