Estimation of Leaf Moisture Content in Cantaloupe Canopy Based on SiPLS-CARS and GA-ELM

Table 1. Prediction results of ELM model with different kernel functions
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Table 1. Prediction results of ELM model with different kernel functions
核函数 R_c RMSEC R_p RMSEP
sig 0.898 6 0.009 3 0.879 7 0.011 4
sin 0.582 9 0.017 4 0.327 7 0.024 5
hardlim 0.829 8 0.013 2 0.828 0 0.013 8

Table 2. Results of subinterval optimization

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Table 2. Results of subinterval optimization

主成分数	优选区间	RMSECV
4	[9, 14, 18]	0.010 97
4	[9, 14, 17]	0.010 98
4	[9, 11, 14]	0.011 13
3	[9, 14, 16]	0.011 13
4	[9, 10, 14]	0.011 15
4	[9, 13, 14]	0.011 23
5	[9, 12, 14]	0.011 24
3	[9, 14, 15]	0.011 26
5	[4, 9, 14]	0.011 31
4	[4, 9, 17]	0.011 40

Table 3. Modeling results of leaf moisture content prediction

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Table 3. Modeling results of leaf moisture content prediction

波长筛选方式	变量个数	建模方式	校正集		预测集
波长筛选方式	变量个数	建模方式	R_c	RMSEC	R_p	RMSEP
		ELM	0.858 8	0.011 69	0.832 5	0.012 57
SiPLS	273	GA-ELM	0.923 0	0.008 80	0.870 5	0.011 49
		PSO-ELM	0.887 6	0.009 88	0.845 3	0.013 35
		ELM	0.873 7	0.010 81	0.845 0	0.012 53
SiPLS-CARS	20	GA-ELM	0.928 9	0.008 48	0.903 2	0.010 64
		PSO-ELM	0.902 8	0.009 58	0.883 9	0.011 09
		ELM	0.873 9	0.010 75	0.834 2	0.012 11
SiPLS-GA	32	GA-ELM	0.925 8	0.008 48	0.887 7	0.010 38
		PSO-ELM	0.897 0	0.010 12	0.850 7	0.011 01
		ELM	0.886 0	0.010 38	0.836 0	0.012 58
SiPLS-SPA	6	GA-ELM	0.928 1	0.008 42	0.881 0	0.010 19
		PSO-ELM	0.891 3	0.010 04	0.884 8	0.011 01

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Yang GUO, Jun-xian GUO, Yong SHI, Xue-lian LI, Hua HUANG, Yan-cen LIU. Estimation of Leaf Moisture Content in Cantaloupe Canopy Based on SiPLS-CARS and GA-ELM[J]. Spectroscopy and Spectral Analysis, 2022, 42(8): 2565

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