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

Abstract

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.