The objective of this work was to develop a model for simulating the leaf color dynamics of winter wheat in relation to crop growth stages and leaf positions under different nitrogen (N) rates.  RGB (red, green and blue) data of each main stem leaf were collected throughout two crop growing seasons for two winter wheat cultivars under different N rates.  A color model for simulating the leaf color dynamics of winter wheat was developed using the collected RGB values.  The results indicated that leaf color changes went through three distinct stages, including early development stage (ES), early maturity stage (MS) and early senescence stage (SS), with respective color characteristics of light green, dark green and yellow for the three stages.  In the ES stage, the R and G colors gradually decreased from their initial values to steady values, but the B value generally remained unchanged.  RGB values remained steady in the MS, but all three gradually increased to steady values in the SS.  Different linear functions were used to simulate the dynamics of RGB values in time and space.  A cultivar parameter of leaf color matrix (M_RGB) and a nitrogen impact factor (FN) were added to the color model to quantify their respective effects.  The model was validated with an independent experimental dataset.  RMSEs (root mean square errors) between the observed and simulated RGB values ranged between 7.0 and 10.0, and relative RMSEs (RRMSEs) ranged between 7 and 9%.  In addition, the model was used to render wheat leaves in three-dimensional space (3D).  The 3D visualizations of leaves were in good agreement with the observed leaf color dynamics in winter wheat.  The developed color model could provide a solid foundation for simulating dynamic crop growth and development in space and time. 

The objective of this work was to develop a dynamic model for describing leaf curves and a detailed spatial geometry model of the rice leaf (including sub-models for unexpanded leaf blades, expanded leaf blades, and leaf sheaths), and to realize three-dimensional (3D) dynamic visualization of rice leaves by combining relevant models.  Based on the experimental data of different cultivars and nitrogen (N) rates, the time-course spatial data of leaf curves on the main stem were collected during the rice development stage, then a dynamic model of the rice leaf curve was developed using quantitative modeling technology.  Further, a detailed 3D geometric model of rice leaves was built based on the spatial geometry technique and the non-uniform rational B-spline (NURBS) method.  Validating the rice leaf curve model with independent field experiment data showed that the average distances between observed and predicted curves were less than 0.89 and 1.20 cm at the tilling and jointing stages, respectively.  The proposed leaf curve model and leaf spatial geometry model together with the relevant previous models were used to simulate the spatial morphology and the color dynamics of a single leaf and of leaves on the rice plant after different growing days by 3D visualization technology.  The validation of the leaf curve model and the results of leaf 3D visualization indicated that our leaf curve model and leaf spatial geometry model could efficiently predict the dynamics of rice leaf spatial morphology during leaf development stages.  These results provide a technical support for related research on virtual 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, MatRGB (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 (da) were among 3.85 to 6.90, and the ratio of da to the mean observation values (dap) 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.