Research on Monitoring Topping Time of Cotton Based on AdaBoost+Decision Tree

Table 3

The regression table of the combinations band and the cotton of plant height, buds, and fruiting branches.

Input band	Fitting index	Method	Fitting result
			R^2	RMSEP
730 + 790 nm	Cotton plant height	Multivariate linear regression	0.17	3.95
		Neural network	0.91	3.04
		Support vector machine	0.86	3.50
		Decision tree	0.90	3.91
	Number of buds	Multivariate linear regression	0.46	0.48
		Neural network	0.84	0.39
		Support vector machine	0.80	0.73
		Decision tree	0.89	0.30
	Number of fruiting branches	Multivariate linear regression	0.66	0.49
		Neural network	0.80	0.32
		Support vector machine	0.65	0.89
		Decision tree	0.90	0.32
550 + 730+790 nm	Cotton plant height	Multivariate linear regression	0.46	0.44
		Neural network	0.95	0.22
		Support vector machine	0.96	0.55
		Decision tree	0.91	0.91
	Number of buds	Multivariate linear regression	0.46	0.44
		Neural network	0.95	0.88
		Support vector machine	0.96	0.55
		Decision tree	0.91	0.98
	Number of fruiting branches	Multivariate linear regression	0.44	0.89
		Neural network	0.80	0.84
		Support vector machine	0.65	0.18
		Decision tree	0.91	0.75
Full band spectra	Cotton plant height	Multivariate linear regression	0.44	0.97
		Neural network	0.87	0.52
		Support vector machine	0.17	0.45
		Decision tree	0.89	0.87
	Number of buds	Multivariate linear regression	0.46	0.47
		Neural network	0.89	0.45
		Support vector machine	0.18	0.02
		Decision tree	0.89	0.38
	Number of fruiting branches	Multivariate linear regression	0.66	0.49
		Neural network	0.97	0.63
		Support vector machine	0.17	0.11
		Decision tree	0.89	0.96