Modeling Dynamics of Leaf Color Based on RGB Value in Rice

This paper was to develop a model for simulating the leaf color changes in rice (Oryza sativa L.) based on RGB (red, green, and blue) values. Based on rice experiment data with different cultivars and nitrogen (N) rates, the time-course RGB values of each leaf on main stem were collected during the growth period in rice, and a model for simulating the dynamics of leaf color in rice was then developed using quantitative modeling technology. The results showed that the RGB values of leaf color gradually decreased from the initial values (light green) to the steady values (green) during the first stage, remained the steady values (green) during the second stage, then gradually increased to the final values (from green to yellow) during the third stage. The decreasing linear functions, constant functions and increasing linear functions were used to simulate the changes in RGB values of leaf color at the first, second and third stages with growing degree days (GDD), respectively; two cultivar parameters, Mat(RGB) (leaf color matrix) and AR (a vector composed of the ratio of the cumulative GDD of each stage during color change process of leaf n to that during leaf n drawn under adequate N status), were introduced to quantify the genetic characters in RGB values of leaf color and in durations of different stages during leaf color change, respectively; FN (N impact factor) was used to quantify the effects of N levels on RGB values of leaf color and on durations of different stages during leaf color change; linear functions were applied to simulate the changes in leaf color along the leaf midvein direction during leaf development process. Validation of the models with the independent experiment dataset exhibited that the root mean square errors (RMSE) between the observed and simulated RGB values were among 8 to 13, the relative RMSE (RRMSE) were among 8 to 10%, the mean absolute differences (d(a)) were among 3.85 to 6.90, and the ratio of d(a) to the mean observation values (d(ap)) were among 3.04 to 4.90%. In addition, the leaf color model was used to render the leaf color change over growth progress using the technology of visualization, with a good performance on predicting dynamic changes in rice leaf color. These results would provide a technical support for further developing virtual plant during rice growth and development.