Varieties discrimination results of oat seeds using different models^a.

Models	Parameters	Training		Testing
		Accuracy/%	Time/s	Accuracy/%	Time/s
Full wavelengths + RBF_SVM	(256, 0.0039)	98.63	13.97	98.05	1.97
Full wavelengths + LINEAR_SVM	—	98.39	18.02	97.88	0.0026
Full wavelengths + LR	(liblinear, L2, 256)	98.94	11.63	98.69	0.0024
Optimal wavelengths + RBF_SVM	(256, 0.16)	89.82	4.58	87.31	0.22
Optimal wavelengths + LINEAR_SVM	—	84.62	6.38	84.21	0.0017
Optimal wavelengths + LR	(Liblinear, L2, 74.66)	85.30	3.83	84.92	0.0008
Deep spectral features + RBF_SVM	(1.85, 0.0039)	100	19.04	99.05	2.25
Deep spectral features + LINEAR_SVM	—	100	11.81	99.02	0.61
Deep spectral features + LR	(Liblinear, L2, 0.54)	100	18.02	98.72	0.0050
DCNN trained in end-to-end manner	(256, 133)	100	9701.63	99.19	7.96

^aParameters of different discriminant models. (c, g) for RBF_SVM, (optimize_algo, r, c′) for LR, and (epoch) for DCNN trained in end-to-end manner.