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2 小麦单茎穗茎秆和麦穗形态参数与单穗籽粒产量回归模型

Table 2 The regression model between stem & ear morphological parameters per stem-panicle and ear-derived grain yield of wheat

拟合模型 Fitting model 模型类型 Model type 模型方程 Model equation 麦穗形态参数与单穗籽粒产量 Ear morphological parameters and ear-derived grain yield 线性Linear SEY=a0+a1×SEL+a2×SEAW+a3×SEA+a4×SEC 二次Quadratic SEY=a0+a1×SEL2+a2×SEAW2+a3×SEA2+a4×SEC2 拓展Extended SEY=a0+a1×SEL2+a2×SEAW2+a3×SEA2+a4×SEC2+a5×SEL×SEAW+a6×SEL×SEA+a7×SEL×SEC+... 指数Exponential ln(SEY)=a0+a1×ln(SEL)2+a2×ln(SEAW)2+a3×ln(SEA)2+a4×ln(SEC)2 茎秆和麦穗形态参数与单穗籽粒产量 Stem and ear morphological parameters and ear-derived grain yield 线性Linear SEY=a0+a1×SEL+a2×SEAW+a3×SEA+a4×SEC+a5×SSL+a6×SSAW+a7×SSA+a8×SSC 二次Quadratic SEY=a0+a1×SEL2+a2×SEAW2+a3×SEA2+a4×SEC2+a5×SSL2+a6×SSAW2+a7×SSA2+a8×SSC2 拓展Extended SEY=a0+a1×SEL2+a2×SEAW2+a3×SEA2+a4×SEC2+a5×SSL2+a6×SSAW2+a7×SSA2+a8×SSC2+a9× SEL×SEAW+... 指数Exponential ln(SEY)=a0+a1×ln(SEL)2+a2×ln(SEAW)2+a3×ln(SEA)2+a4×ln(SEC)2+a5×ln(SSL)2+a6×ln(SSAW)2+a7× ln(SSA)2+a8×ln(SSC)2