Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Table 4 Performance of grain yield prediction on testing data, using variable sets determined from LASSO and random forest, as well as all available variables

Variables	LASSO selected variables		Random forest selected variables		All 172 variables
Sample size	r*	RMSE* (g/plot)	r	RMSE (g/plot)	r	RMSE (g/plot)
(1) Predictions of SVM model with Gaussian radial basis kernel
All lines	0.32	320.19	0.39	306.15	0.29	314.77
NE lines	0.58	326.97	0.77	254.66	0.72	284.08
TX lines	0.21	271.44	0.36	255.51	0.57	215.92
WB lines	0.28	271.88	0.41	236.82	0.25	264.53
OK and SY lines	0.39	201.45	0.45	191.06	0.36	193.31
(2) Predictions of ridge regression model
All lines	0.49	283.86	0.39	301.89	0.25	314.83
NE lines	0.73	272.72	0.81	225.45	0.73	295.92
TX lines	0.55	235.37	0.50	255.68	0.47	242.99
WB lines	0.40	247.57	0.42	246.21	0.22	266.25
OK and SY lines	0.59	163.69	0.54	164.90	0.58	169.29

ISSN: 1746-4811