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Table 4 Constructed correlation models between the SPAD value and the leaf color parameters

From: Skewed distribution of leaf color RGB model and application of skewed parameters in leaf color description model

Model Fit type
F1=59.733 − 0.304 × RMean Linear regression
F2 = 76.134 − 0.441 × RMean − 11.203 × YSkewness − 1.516 × GKurtosis Linear regression
F3 = 19.38 + 7.972 × cos (1.314 × RMedian) − 6.747 × sin (1.314 × RMedian) Fourier fitting
F4 = 0.3344 + 0.8709 × RMean − 1 77.3 × RSkewness − 0.005536 × R 2Mean
+2.8 76 × RMean × RSkewness + 8.515 × R 2Skewness  − 0.0122 7 × R 2Mean  × RSkewness − 0.1398 × RMean × R 2Skewness  + 7.301 × R 3Skewness
Polynomial fitting
  1. F1: Using the mean parameters RMean, GMean, BMean and their combinations with a normality assumption to establish multivariate linear regression models by stepwise regression, then choosing the best model. F2: Using all 20 parameters to establish multivariate linear regression models by stepwise regression, then choosing the best model. F3: Using the Fourier function to fit and obtain the model. F4: Using the MATLAB Curve Fitting Toolbox to fit the polynomial F4 that incorporates spatial bidirectional patterns