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Modeling leaf color dynamic in rice plant based on spad value

机译:基于Spad值的水稻叶片颜色动态建模

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Modeling leaf color dynamics in rice (Oryza sativa L.) is an important task for realizing virtual and digital plant growth. This study was undertaken to analyze the dynamics of leaf color changes at different leaf positions on main stems and tillers under different growing conditions, and to build a dynamic simulation model on leaf color changes in rice plant in relation to growing degree-days (GDD). Time-course observations were made on leaf color (in SPAD value) changes at different leaf positions of stem and tillers under different nitrogen rates and water regimes with four rice cultivars. Leaf color changes in SPAD could be described in three phases. The first phase during leaf elongation period was based on the exponential relationship of leaf color to cumulative GDD; the second phase during leaf function period was represented with a relative stable SPAD; the third phase during leaf senescence period was described in a quadratic equation between SPAD and GDD. In addition, the effects of nitrogen and water conditions on leaf color were quantified through the effectiveness values of leaf nitrogen concentration and water content in relation to SPAD. Then, the RGB (red, green, and blue) values were further predicted from the changing SPAD during leaf development. The model was validated with the independent field experiment data involving different rice cultivars and nitrogen rates. The average root mean square errors (RMSE) between the simulated and observed SPAD values at different leaf positions were 2.58, 3.69 and 3.82, respectively, for three leaf color phases on main stem; 4.65, 4.39, 3.51 and 4.25, respectively, for four individual tillers; and 2.98 and 3.25, respectively, for SPAD and R, G values in rice. The results indicate that the present model has a good performance in predicting leaf color changes at different leaf positions in rice under various growth conditions, and thus lays a foundation for further constructing digital and visual rice growth sys--tem.
机译:水稻(Oryza sativa L.)叶片颜色动态建模是实现虚拟植物和数字植物生长的重要任务。本研究旨在分析不同生长条件下主茎和分ers上不同叶片位置叶片颜色变化的动态,并建立与生长日数(GDD)相关的水稻叶片颜色变化的动态模拟模型。 。时程观察了四个水稻品种在不同氮素水平和水分制度下茎和分ers不同叶片位置的叶片颜色(SPAD值)变化。 SPAD中叶片颜色的变化可以分三个阶段进行描述。叶片伸长期的第一阶段基于叶片颜色与累积GDD的指数关系。叶片功能期的第二阶段表现为相对稳定的SPAD。在叶片衰老期间的第三阶段由SPAD和GDD之间的二次方程式描述。另外,通过相对于SPAD的叶氮浓度和水分含量的有效性值,定量了氮和水分条件对叶片颜色的影响。然后,从叶片发育过程中不断变化的SPAD进一步预测RGB(红色,绿色和蓝色)值。该模型已通过涉及不同水稻品种和氮素含量的独立田间试验数据进行了验证。在主茎上的三个叶片颜色阶段,在不同叶片位置上模拟和观察到的SPAD值之间的平均均方根误差(RMSE)分别为2.58、3.69和3.82。四个单独的分ers分别为4.65、4.39、3.51和4.25;稻中SPAD和R,G值分别为2.98和3.25。结果表明,该模型在预测不同生长条件下水稻不同叶片位置的叶色变化方面具有良好的预测性能,从而为进一步构建数字化和可视化水稻生长系统奠定了基础。 -- tem。

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