本研究的目标是构建冬小麦叶片颜色动态模型，以模拟不同生育期和施氮水平下小麦不同叶位叶色变化。基于不同品种和施氮量下两个生长季的冬小麦试验，获取各主茎叶位叶片颜色的RGB（红、绿、蓝）数据。基于获取的RGB数据，构建了冬小麦叶片颜色动态模拟模型。结果表明，冬小麦叶片颜色变化经历了早期发育期（ES）、早熟期（MS）和早衰期（SS）三个不同的阶段，三个阶段的颜色特征分别为浅绿、深绿、黄色。在ES期，R和G颜色从初始值逐渐下降到稳定值，而B值基本保持不变。RGB值在MS阶段保持稳定，但在SS阶段三个值会逐渐增加到稳定值。采用不同的线性函数来模拟RGB值在时间和空间上的动态变化，在叶色模型中引入了品种参数（叶色矩阵M_RGB）和氮素影响因子（FN）来量化它们各自的影响。利用独立的试验数据集对模型进行了检验，实测值与模拟值的均方根误差（RMSE）在7.0-10.0之间，相对RMSE（RRMSEs）在7%-9%之间。将叶色模型应用于冬小麦叶片的三维模拟，叶色可视化结果与叶色实际变化较为一致。此叶色模型可为作物在时空上生长发育的模拟提供坚实基础。

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.