Modeling Dynamics of Leaf Color Based on RGB Value in Rice

Transcription

1 Journal of Integrative Agriculture Advanced Online Publication: 2013 Doi: /S (13) Modeling Dynamics of Leaf Color Based on RGB Value in Rice ZHANG Yong-hui, TANG Liang, LIU Xiao-jun, LIU Lei-lei, CAO Wei-xing and ZHU Yan * National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Jiangsu 295, P.R.China Abstract 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. Key words: rice, leaf color, RGB, dynamic simulation, visualization 1 INTRODUCTION Virtual crop, which simulates crop growth and development in three-dimensional (3D) space by computer, has wide application prospect in the fields of crop production, crop breeding, plant type design, teaching and virtual experimentation, and has been an interesting theme of research in recent years (Birch et al. 3; Cao et al. 8). Correspondence ZHU Yan, Tel: ; Fax: , yanzhu@njau.edu.cn

2 Crop leaf is a vital vegetative organ, and leaf color is a typical appearance character of crop, which is affected by crop growth conditions (Zhao et al. 6) and can help to understand crop growth status (Singh et al. 2; Yang et al. 3; Witt et al. 5) in order to conduct corresponding management for crop. Therefore, simulation of crop leaf color is very important for the verisimilitude and its application to crop production. RGB (red, green, and blue) is an important index for color expression, and has been widely applied in agricultural production for recognizing crops and weeds (Tillet et al. 1; Aitkenhead et al. 3; Ahmad et al. 6; Lee et al. 2011), analyzing crop seed color (Dana et al. 8), and diagnosing crop nutrition status (Zhu et al. 9; Song et al. 2010). Meanwhile, RGB values of crop leaf color were initially simulated with leaf SPAD value (Zhu et al. 8; Chang et al. 9), which was useful for the dynamic color rendering of virtual crop. However, most of the previous studies on virtual crop paid much attention to the morphological modeling of crop leaf, and neglected the simulation of leaf color (Fournier and Andrieu 1998; Kaitaniemi et al. 0; Fournier et al. 3; Hanan et al. 3). Rice is a staple food crop and is of substantial importance for food security in many countries, and thus has been an important research object in virtual crop. Recently, there have been several research reports on development of virtual rice. Morphological modeling and visualization of rice leaf were realized with the methods of Non-Uniform Rational B-Splines (NURBS) (Liu et al. 4), Bezier curve (Yang et al. 8), and digital image processing technology (He et al. 8); meanwhile, the visualization of the single rice plant was realized using L-studio based on plant morphological models (Watanabe et al. 5), and based on analyzing rice morphological characters during rice growth (Ding et al. 2011); but the changes in leaf color were not systemically studied in these studies. Besides, the average values of RGB in rice leaf color were initially analyzed by quantifying their relationships to the average values of SPAD (soil and plant analyzer development) of rice leaf (Zhu et al. 2010). Yet these indirect methods could increase prediction errors in simulation of RGB values for leaf color changes. Previous studies have not addressed quantitative simulation on rice leaf color with different cultivars and N rates directly based on RGB values. Meanwhile, our experiment data indicated that there were some regular changing patterns of leaf color with time (growth process) and space (along the leaf midvein direction) during leaf development process, and the durations of different stages during leaf color change were also affected by N levels. All of these justified a further investigation on quantifying and modeling leaf color change and its visualization in rice. Therefore, this study was undertaken to develop a simulation model for predicting the dynamics of leaf color in rice with different cultivars and N levels, based on RGB values. The expected results can provide a key technology to support further development of virtual rice and its application in agricultural production. MATERIALS AND METHODS Experimental design Experiment 1: The experiment was conducted in 2010 at the Experiment Station of Nanjing Agricultural University (118º'E, 32º02'N), China. Two rice (Oryza sativa L.) cultivars, Wuxiangjing 14 (W14, japonica cultivar), and Yangdao 6 (YD6, indica cultivar), were sown by direct seeding on 1 June. The culture barrel was 35 cm in diameter at the upper underside, 20 cm at the lower underside, and 40 cm in height, respectively. Two seedlings were kept per barrel for both cultivars. Four N rates of as 0 g (N1), 1.5 g (N2), 3.1 g (N3), and 4.6g (N4)

3 for each barrel were applied, with N distributed as % at pre-transplanting, 10% at tillering, 20% at jointing, and 20% at booting. For both cultivars under different N rates, phosphorus (P 2O 5), and potassium (K 2O) were applied as basal at 0.8 g and 1.53 g for each barrel, respectively. Other field managements followed local practices for high yield in rice. Experiment 2: The experiment was conducted in 2011 at the Experiment Station of Nanjing Agricultural University (118º'E, 32º02'N), China. The same two cultivars as in Experiment 1 were planted on 26 May, and transplanted on 13 June at 28 cm 20 cm spacing for YD6 and 20 cm 15 cm for W14 with one seedling per hill for each cultivar. Three N rates as 0 kg ha -1 (N1), 125 kg ha -1 (N2) and 2 kg ha -1 (N3) were applied, with N distributed as % at pre-transplanting, 10% at tillering, 20% at jointing, and 20% at booting. For both cultivars under different N rates, phosphorus (P 2O 5) and potassium (K 2O) were applied as basal at 82.5 kg ha -1, and kg ha -1, respectively. All the other management measures were applied according to the local cultural practices for high yield in rice. Data acquisition According to the changing speed of leaf color in rice, 4-6 plants (2-3 barrels) were randomly selected as samples every 2-7 d. Firstly, a scanner EPSON Perfection 1 PHOTO (EPSON, Japan), with the image size of pixels, 24 bits color depth, and other default settings was used to scan leaves on main stem of rice. Then, RGB values (0-255) of leaf were abstracted from the basal to the distal along leaf midvein from the scanned pictures. Each leaf was equally divided into about 5 to 10 segments with the length of about 1 to 5 cm (the length of segments on the same leaf was same) along the leaf midvein. RGB values of leaf in each segment were the average RGB values of pixel points in this segment. Finally, the RGB values were calibrated by a color correction method based on the standard whiteboard (Cheng et al. 7). During the whole rice growth cycle, air temperature was recorded daily at 30-min intervals with the ZDR-11 (made at Zhejiang University of China), which was downloaded every month and used for calculating growing degree days (GDD, d). In addition, the N contents and dry weights of different organs of rice samples were measured. Date analysis The data of Experiment 1 and 2 were used to build and validate the models, respectively. Data fitness, variance analysis, and color rendering were done by the softwares of Excel.7, CurveExpert 1.4, MatlabR9a, and Visual Studio 5. The fitness between the simulated and observed values was calculated with the root mean square error (RMSE) and the relative RMSE (RRMSE) (Rinaldi et al., 3), as well as the mean absolute difference (d a) and the ratio of d a to the mean observation value (d ap) (Cao et al. 2012). RESULTS Modeling the dynamics of leaf color with GDD Leaf color changed obviously during the process of leaf appearance, expansion, maintenance, and senescence in rice, and followed the pattern of light green-green-yellow (Fig. 1). The experiment data showed that the

4 changing process of RGB (red, green, and blue) values of leaf color could be divided into three stages: gradually decreased to the steady values from the initial values at the first stage, remained the steady values at the second stage, and gradually increased to the final values at the third stage (Figs. 2-4). According to our experimental data, if the cumulative GDD from the beginning to the end of elongation of leaf n was defined as GDD n, ( GDD n), ( GDD n), and ( GDD n) were generally needed during the first, the second, and the third stages, respectively. In addition, the duration of each stage was affected by N application rate and cultivar (Fig. 2). The dynamics of RGB value of leaf top segments at different leaf positions on main stem in rice under the same N rate were similar (Fig. 3), and no significant differences were observed on the initial, the steady, and the final RGB values of leaf top segments at different leaf positions in the same rice cultivar under the same N rate by variance analysis of experiment data, respectively (P>0.05). Change trends in RGB values of leaf top segment at the same leaf position on main stem in rice under different N rates were also similar (Fig. 2), and no significant differences were observed on the initial and the final RGB values of leaf top segments at different leaf positions on main stem in rice under different N rates by the variance analysis of experiment data, respectively (P>0.05), but the steady RGB values during the second stage decreased obviously with increasing N rates (Fig. 2). Besides, the changes in RGB values of different leaf segments along leaf midvein from the distal to the basal were basically identical (Fig. 4), and no significant differences were observed (P>0.05). According to the above analysis, equation (1) was used to quantify the linear changes of RGB values of the top segment of leaf n with GDD (the simulation of RGB values of the whole leaf would be expressed in the following section). TRGBI ( TRGBS TRGBI ) M1, IGDDn GDD IGDDn C1 GDDn ; TRGBS, IGDDn C1 GDDn GDD IGDDn ( C1 C2) GDDn ; TRGB( n, GDD) TRGBS ( TRGBF TRGBS ) M 2, IGDDn ( C1 C2) GDDn GDD IGDDn ( C1 C2 C3) GDDn ; TRGBF, GDD IGDDn ( C1 C2 C3) GDD n. (1) Where GDD denotes growing degree days, calculated above a basal temperature (10 for Japonica and 12 for Indica cultivars) for development from the appearance of the first leaf above soil surface (Gao et al. 1987); IGDD n is the GDD at the initial elongation of leaf n, calculated by eq. (2); GDD n is the cumulative GDD from the beginning to the end of elongation of leaf n, calculated by eq. (3). IGDD n n a 1 1 b (2) 1 1 b b n n1 GDDn a a (3) Where n is the leaf position on main stem; a, and b were equation coefficients with , and (R 2 =0.9847) for YD6 and , and (R 2 =0.9837) for W14, respectively (Zhang et al. 2012). TRGB (n, GDD) in equation (1) denotes the RGB vector composed of RGB values (TR(n, GDD), TG(n, GDD), and TB(n, GDD)) of the top segment of leaf n at GDD, calculated by eq. (4).

5 TR( n, GDD) TRGB( n, GDD) TG( n, GDD) (4) TB( n, GDD) TRGB I, TRGB S, and TRGB F in equation (1) denote the initial, the steady, and the final RGB vectors of the top segment of leaf n, calculated by eqs. (5) to (7), respectively. TRGBI Mat RGB (:,1) (5) Mat TRGB RGB(:,2) S (6) FN TRGBF Mat RGB (:,3) (7) Where Mat RGB is the leaf color matrix, assigned by the average RGB values of all leaves on main stem in rice under adequate N condition. According to our experiment data, Mat RGB varied with rice cultivars, thus it was set as a cultivar parameter and denoted by eq. (8). Mat RGB ARI ARS ARF AGI AGS AGF (8) AB I ABS AB F Where AR I, AG I, and AB I are the initial RGB values, AR S, AG S, and AB S are the steady RGB values, and AR F, AG F, and AB F are the final RGB values of leaves on main stem in rice under adequate N condition, respectively According to our experiment data, Mat RGB were set to , and for YD6 and W14, respectively. FN in eq. (6) is the N impact factor, calculated by equation (9). ANCSH, ANCSH MNCSH FN MNCSH 1, ANCSH MNCSH (9) Where ANCSH is the actual N content; MNCSH is the optimum N concentration, calculated by equation (10). MNCSH c AGB d (10) where AGB is the biomass of above-ground plant; c and d are equation coefficients with 5.18 and 0.52, respectively (Confalonieri et al. 2011). M 1 and M 2 in equation (1) denote the normalized GDDs at the first and the third stages, respectively, calculated by equation (11). M 1 GDD IGDD C GDD 1 n n GDD IGDD ( C C ) GDD n 1 2, M 2 C 3 GDD n n (11) Where C 1, C 2, and C 3 denote the ratio of cumulative GDD of each stage during color change process of leaf n on main stem to GDD n, respectively, affected by cultivar and N rate (Figs. 2 and 5). Our experiment data showed that C 1, C 2, and C 3 enhanced linearly with increasing N rates until N was adequate, with no significant difference between different leaves on main stem under the same N rate (P>0.05), respectively (Fig. 5); therefore, C 1, C 2, and C 3 were calculated by equation (12).

6 C1 AR1 C2 FN AR2 (12) C AR 3 3 Where AR 1, AR 2, and AR 3 denote the ratio of cumulative GDD of each stage during color change process of leaf n to GDD n under adequate N condition, respectively. Our experiment data showed that C 1, C 2, and C 3 distributed between [2.25, 2.48], [5.75, 6.20], and [3.45, 3.72] for YD6, as well as [2.68, 2.86], [6.70, 7.15], and [4.02, 4.29] for W14 under high N rate (N3, N4), fluctuating with small amplitude around 2.4, 6.0, and 3.6 for YD6, as well as 2.8, 7.0, and 4.2 for W14 under high N rate, respectively (Fig. 5); therefore, the vector AR=(AR 1, AR 2, AR 3) was set to (2.4, 6.0, 3.6) and (2.8, 7.0, 4.2) for YD6 and W14, respectively; and set as a cultivar parameter. Modeling leaf color changes along leaf midvein Because of the differences of drawn timings between different leaf segments along leaf midvein, leaf color changed with an identical trend along leaf midvein, but the change timings were asynchronous between different segments during the first stage and the third stage (Fig. 4). Furthermore, the relationship between the relative time of each segment color change (RT, the ratio of the difference between GDD of each segment and GDD of the top segment to GDD n, when RGB arrived to the steady or the final values) and its relative position (RP, the ratio of the length from each segment center to leaf tip to the whole length of leaf n) was analyzed (Fig. 6). The result showed that the changes in RGB values of leaf color along the leaf midvein from the distal to the basal were nearly linear with GDD. Therefore, eq. (13) was used to calculate the RGB values of each point (E) in leaf at GDD time, where point (E) was on line segment (CD), CD was perpendicular to leaf midvein (AB) (Fig. 7). To simplify the model, RGB values of pixel points on the same line segment (CD) perpendicular to leaf midvein (AB) direction were assumed equal. r GDDn LRGB( r, GDD) TRGB( GDD IGDDn ); (13) LL where LRGB(r, GDD) denoted RGB values of any point (E) in leaf at GDD; LL was the length of leaf; r denoted the distance from the line segment (CD) to the basal of leaf (B); other parameters were the same as above. Model validation The data of the top (Top), the middle (Mid), and the bottom (Bot) segments of the 2 th to 8 th leaves on main stem at tillering stage, and the 9 th to15 th leaves on main stem at jointing stage under three N rates in experiment 2 were selected to validate the above leaf color model. Comparisons of the simulated and measured RGB values showed that RMSE were (Fig. 8-A), and (Fig. 8-B) at tilling stage for YD6, and W14, (Fig. 9-A), and (Fig. 9-B) at jointing stage for YD6, and W14, respectively; RRMSE were 9.30% (Fig. 8A), and 9.54% (Fig. 8B) at tilling stage for YD6, and W14, 8.23% (Fig. 9-A), and 9.13% (Fig. 9-B) at jointing stage for YD6, and W14, respectively; d a were 4.57 (Fig. 8A) and 6.32 (Fig. 8-B) at tilling stage for YD6 and W14, and 3.85 (Fig. 9-A) and 6.90 (Fig. 9-B) at jointing stage for YD6 and W14, respectively; d ap were 3.53% (Fig. 8-A) and 4.90% (Fig. 8-B) at tilling stage for YD6 and W14, and 3.04% (Fig. 9-A) and 4.59% (Fig. 9-B) at jointing stage for YD6 and W14, respectively. These results indicated that the present models had a good performance on predicting the dynamics of leaf color change with GDD and the differences of leaf color along leaf midvein.

7 Model application Combining the above models of leaf color, and using the programming technology of C# and OpenGL on platform.net, a visualization of the leaf color change with time (growing process) and space (along leaf midvein direction) was realized in the computer Hp Z600 (Intel Xeon E54 2.0G CPU, 512M NVIDIA Quadro FX 580 Display card, 3GB Memory). Figs. 10 and 11 were the visualization results of the 5th leaf on main stem in YD6 under different N rates, which were relatively realistic with a good prediction on leaf color changes. The leaf color differences between different N rates were well embodied: the higher N rate, the deeper leaf color at the second stage, and the longer the duration of each stage during the change process of leaf color. DISCUSSION Crop leaf color was easily affected by environmental factors, thus the data of leaf color were a little difficult to obtain. In our study, RGB data of different leaf parts along the leaf midvein direction during leaf development were collected by strict operation, and were calibrated by the method of Cheng et al. (7). These measurements can ensure the relative precision of RGB data compared with those in previous studies (Zhu et al. 2010; Lee et al. 2011). The leaf color model in this study could be further combined with diagnosis methods of chlorophyll content (Yadav et al. 2010) and lipid content (Su et al. 8) for quantitatively assessing leaf N status in rice. Rice leaf color showed an apparent change pattern during appearance, expansion, maintenance, and senescence stages of leaf, which was affected by cultivars. Our experiment data and previous study showed that between different cultivars, differences were observed on leaf color (Zhao et al. 6) and on the duration of each stage during leaf color change process; and the initial, the steady, and the final RGB values of leaf color as well as the relative duration of each stage during color change process of leaf n of the same cultivar under adequate N condition were generally steady. Therefore, the two cultivar parameters (Mat RGB and AR) were introduced into the model to quantify the differences of leaf color and the duration of each stage between cultivars, which were useful for improving the applicability of the model to different cultivars. Previous studies have provided a qualitative description that the duration of leaf functional period increased with increasing N rate in rice (Wang et al. 3; Nie et al. 5) and the leaf color in rice became deeper with increasing N rate, but no quantitative result was reported (Zhao et al. 6). With the similar case, our experiment data showed that before arriving the adequate N condition, the cumulative GDD of each stage during color change process of leaf n increased with increasing N rate, and the steady RGB values of leaf during the second stage decreased with increasing N rate, but the initial and final values of RGB of leaf color were little affected by N rate. Therefore, FN was introduced into the model to quantify the effect of N level on the duration of each stage during leaf color change process and on RGB values of leaf color during the second stage, which could improve the result about N effect on duration of each stage (Zhu et al. 2010). Color change from green to yellow was an obvious characteristic of leaf senescence (Liang et al. 6), therefore, the color model in this study could be used to explore approach to prolonging the duration of leaf functional period and delaying leaf senescence by adjusting N rate for enhancing rice productivity. Besides, the color model could be applied to diagnosis on growth status of rice crop based on the method of RGB values (Su et al. 8; Yadav et al. 2010), which would promote the potential application of virtual rice in agriculture production. After a period of biomass accumulation, leaf color changed from light green to green along leaf midvein

8 from the distal to the basal at the first stage; then generally sustained green at the second stage; finally, changed from green to yellow along leaf midvein at the third stage. There was similar expression about the changes of crop leaf color in previous studies (Zhu et al. 8; Chang et al. 9; Zhu et al. 2010). During the first and the third stages, the timings of biomass transferred into and out different parts were asynchronous, thus the changes in leaf color were not uniform along leaf midvein from the distal to the basal. In this study, the change trend of leaf color along the leaf midvein direction was quantified based on the differences of drawn timings of different parts along leaf midvein, which could provide model support for non-uniform rendering of leaf color. Moreover, with our color model, visualization of leaf color was realized, and the rendering results could embody the changes of leaf color with time (growing process) and space (along leaf midvein direction), as well as the effect of N level on the changes of leaf color, which was more vivid than the previous studies (Liu et al. 4; Watanabe et al. 5; He et al. 8; Yang et al. 8; Yang et al. 9; Ding et al. 2011). The changes in RGB values of leaf color on tillers were not quantified in this study, but the RGB data could be obtained by the synchronous relationships between tillers and main stem in rice. Furthermore, rice leaf color could be affected by other nutrients and water conditions. Yet, the rice experiments in our study were conducted only under appropriate water regimes and with different N rates. Therefore, further investigations are needed to expand the present model with additional cultural factors in order to improve the adaptability of our model under diverse production conditions. CONCLUSION Based on RGB values, the dynamics of leaf color on main stem with time (growing process) and space (along leaf midvein direction) under different rice cultivars and N application rates were simulated by introducing cultivar parameters and N impact factor. Validation of the model with the independent dataset and visualization of the leaf color showed that the present leaf color model could well predict the dynamics of leaf color changes in rice. Acknowledgements The work was supported by the National High-Tech Research and Development Program of China (2013AA404, 2012AA ) and the Priority Academic Program Development of Jiangsu Higher Education Institutions of China (PAPD). References Cao H, Hanan J, Liu Y, Liu Y, Yue Y, Zhu D, Lu J, Sun J, Shi C, Ge D, Wei X, Yao A, Tian P, Bao T Comparison of crop model validation methods. Journal of Integrative Agriculture, 11, Ahmad I, Naeem A M, Islam M. 6. Real-time specific weed recognition system using histogram analysis. World Academy of Science, Engineering and Technology, 16, Aitkenhead M, Dalgetty I, Mullins C, McDonald A., Strachan N. 3. Weed and crop discrimination using image analysis and artificial intelligence methods. Computers and Electronics in Agriculture, 39, Birch C, Andrieu B, Fournier C, Vos J, Room P. 3. Modelling kinetics of plant canopy architecture concepts and applications. European Journal of Agronomy, 19,

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11 R value of leaf color G value of leaf color B value of leaf color R value of leaf color G value of leaf color B value of leaf color the first stage the second stage the third stage Fig. 1 Color changes of the 5 th leaf on main stem in rice cultivar YD6 at different development stages N1YD6 N2YD6 N3YD6 N4YD N1YD6 N2YD6 N3YD6 N4YD N1YD6 80 N2YD6 N3YD6 N4YD N1W14 N1W14 N1W14 N2W14 N2W14 N3W14 80 N2W14 N3W14 N3W14 N4W14 N4W14 N4W Fig. 2 RGB changes of the top segment of the 12th leaf on main stem of YD6 and W14 under different nitrogen (N) rates with growing degree days (GDD).N1YD6 denoted rice cultivar YD6 under N1 treatment in experiment, the meanings of other legends could be known by this regularity.

12 RGB values of leaf color RGB values of l RGB values of leaf color RGB values of l 2 1 N3W14-7R N3W14-7G N3W14-7B N3W14-12R N3W14-12G N3W14-12B A 2 1 N3YD6-7R N3YD6-7G N3YD6-7B N3YD6-12R N3YD6-12G N3YD6-12B B GDD d) ( Fig. 3 RGB changes of the top segment of the 12 th and 7 th leaves on main stem of W14 (A) and YD6 (B) with growing degree days (GDD) under N3 treatment in experiment 1. N3W14-7R denoted R value of the 7 th leaf color in rice cultivar W14 under N3 treatment in experiment, the meanings of other legends could be known by this regularity. 2 YD6-TR YD6-TG YD6-TB YD6-MR YD6-MG YD6-MB YD6-BR YD6-BG YD6-BB A 2 W14-TR W14-TG W14-TB W14-MR W14-MG W14-MB W14-BR W14-BG W14-BB B GDD d) ( Fig. 4 RGB changes of the top (T), the middle (M) and the bottom (B) segments of the 12 th leaf on main stem of YD6 and W14 with growing degree days (GDD) under N3 treatment in experiment 1. YD6-TR denoted the R value of the top segment of leaf in YD6; the meanings of other legends could be known by this regularity.

13 RT RT 8 A 8 B 6 6 Ci (i=1, 2, 3) 4 Ci (i=1, 2, 3) Leaf position on main stem Leaf position on main stem Fig. 5 Changes of C i (the ratio of cumulative GDD of stage i during color change process of leaf n to the cumulative GDD during leaf n drawn, i=1, 2, 3) with leaf position on main stem of different rice cultivars (A: YD6; B: W14) under different nitrogen (N) rates. NjCi (i=1,2,3; j=1,2,3,4) denoted the C i under Nj nitrogen application rate N1YD6 N3YD6 N1W14 N3W14 N2YD6 N4YD6 N2W14 N4W14 A N1YD6 N3YD6 N1W14 N3W14 N2YD6 N4YD6 N2W14 N4W14 B R 2 = R 2 = RP RP Fig. 6 The relationship between the relative time of each segment color change (RT) and its relative position (RP). RT: the ratio of the difference between GDD of each segment and GDD of the top segment to GDDn, when RGB arrived to the steady (Fig. 6A) or the final values (Fig. 6B); RP: the ratio of the length from each segment center to leaf tip to the whole length of leaf n; n were 6 and 13 in Fig. 6A and Fig. 6B, respectively.

14 Simulated values Simulated v Journal of Integrative Agriculture Advanced Online Publication: 2013 Doi: A LL C. E D r B Fig. 7 Relation graph of rice leaf structure N=189 RMES=12.27 RRMSE=9.30% d a =4.57 d ap =3.53% A N=189 RMSE=12.45 RRMSE=9.54% d a =6.32 d ap =4.90% B Measured values Measured v Fig. 8 Comparisons of the measured and simulated RGB values of top (T), middle (M) and bottom (B) segments of leaf on main stem of YD6 (A) and W14 (B) under different nitrogen (N) rates at tillering stage. N1TR denoted R value of the top segment of leaf in rice under N1 treatment, the meanings of other legends could be known by this regularity.

15 Simulated values Simulaed v N=189 RMSE=10.44 RRMSE=8.23% d a =3.85 d ap =3.04% A N=189 RMSE=11.83 RRMSE=9.13% d a =6.90 d ap =4.59% B Measured values Measured v Fig. 9 Comparisons of the measured and simulated RGB values of top (T), middle (M) and bottom (B) segments of leaf on main stem of YD6 (A) and W14 (B) under different nitrogen (N) rates at jointing stage. N1TR denoted R value of the top segment of leaf in rice under N1 treatment, the meanings of other legends could be known by this regularity. 2 days 5 days 8 days 11 days 30 days 32 days 36 days 38 days 40 days Fig. 10 Color changes of the 5 th leaf on main stem of YD6 at different days after leaf emergence under N1 treatment in experiment 2 2 days 5 days 8 days 11 days 32 days 36 days 40 days 42 days 44 days Fig. 11 Color changes of the 5th leaf on main stem of YD6 at different days after leaf emergence under N3 treatment in experiment 2 15