基于机器视觉的稻茬麦单茎穗高通量表型分析

丁启朔,李海康,孙克润,何瑞银,汪小旵,刘富玺,厉翔

High-Throughput Phenotyping of Individual Wheat Stem and Ear Traits with Machine Vision

QiShuo DING,HaiKang LI,KeRun SUN,RuiYin HE,XiaoChan WANG,FuXi LIU,Xiang LI

表6 小麦单茎穗茎秆重、叶重和穗重与单穗籽粒产量回归模型拟合结果

Table 6 Fitting results of wheat single stem weight, sperm-carrying leaf weight & ear weight and ear-derived grain yield regression model

品种 Variety 模型 Model 系数Coefficient 决定系R2 F值 F value P值 P value 误差方差估计 Error variance estimation a0 a1 a2 a3 … 宁麦13 Ningmai 13 线性Linear -0.065 -0.046 0.120 0.778 0.903 141.909 0.000 0.009 二次Quadratic 0.452 0.137 0.038 0.263 … 0.918 80.005 0.000 0.008 拓展Extended 0.452 1.915 -4.978 0.099 … 0.925 54.835 0.000 0.008 指数Exponential 3.182 0.002 -0.003 0.072 … 0.885 117.718 0.000 0.011 鲁原502 Luyuan 502 线性Linear -0.045 0.049 0.092 0.654 0.861 94.656 0.000 0.024 二次Quadratic 0.511 -0.243 0.361 0.159 … 0.840 80.046 0.000 0.027 拓展Extended 0.518 -0.727 -2.887 0.021 … 0.856 42.714 0.000 0.027 指数Exponential 2.924 0.011 0.004 0.064 … 0.681 2.788 0.000 0.061 郑麦9023 Zhengmai 9023 线性Linear 0.059 -0.032 -0.446 0.792 0.863 96.222 0.000 0.033 二次Quadratic 0.512 -0.025 -0.204 0.207 … 0.867 100.115 0.000 0.032 拓展Extended 0.502 2.883 1.752 0.169 … 0.872 48.618 0.000 0.033 指数Exponential 3.242 0.003 -0.021 0.081 … 0.788 56.975 0.000 0.054