Estimation of leaf chlorophyll content of rice using image color analysis

Abstract

Leaf color has been commonly used as an index for crop stress status diagnosis. We have developed a low-cost and nondestructive method that is easy to use to evaluate the chlorophyll content of rice (Oryza sativa L.) using leaf image color analysis. The relationships between the imaging data and leaf pigment content were investigated. There was a highly significant negative relationship between the red (R) and green (G) values in the RGB color space analyzed from leaf image and chlorophyll a (Chl a) content, chlorophyll b (Chl b) content, Chl a + b content, and carotenoid (CAR) content. The G data had a higher correlation coefficient with Chl a, Chl b, Chl a + b, and CAR than the R data. However, no significant relationship was found between the blue (B) value and Chl a, Chl b, Chl a + b, and CAR. Linear and logarithmic correlation functions were used to model the relationship between imaging data and leaf pigment data. Using another set of collected data, significant correlations were observed between the predicted Chl a, Chl b, Chl a + b, CAR-based G data and the measured values. The determination coefficient of the R predicted model of simulated chlorophyll pigment content and observed data was smaller than that of the G predicted model. Comparably, Chl a + b and Chl a could be better predicted than Chl b and CAR from rice leaf image analysis. Combined with our previous study on barley and wheat, this study demonstrated that the chlorophyll content of plants could be nondestructivly evaluated using the leaf image color analysis method.