Deep Learning Application for Predicting Soil Organic Matter Content by VIS-NIR Spectroscopy

Table 1

Prediction accuracy results of different model with the fractional-order derivative (1.5) transformation of soil spectral reflectance data.

Model	Variables	Training		Validation
		R^2	RMSE	R^2	RMSE	RPD
BPN	67	0.831 ± 0.003	4.900 ± 0.062	0.867 ± 0.000	5.382 ± 0.001	2.843 ± 0.001
BPN	203	0.835 ± 0.018	5.107 ± 0.290	0.834 ± 0.018	6.112 ± 0.290	2.465 ± 0.092
MLP	67	0.806 ± 0.021	5.657 ± 0.288	0.861 ± 0.019	5.578 ± 0.364	2.709 ± 0.164
MLP	203	0.890 ± 0.011	4.249 ± 0.199	0.886 ± 0.002	5.044 ± 0.037	2.987 ± 0.022
LeNet5	67	0.872 ± 0.015	4.542 ± 0.224	0.870 ± 0.020	5.304 ± 0.401	2.901 ± 0.220
LeNet5	203	0.902 ± 0.006	3.909 ± 0.114	0.863 ± 0.001	5.471 ± 0.033	2.790 ± 0.022
DenseNet10	67	0.927 ± 0.009	3.376 ± 0.191	0.888 ± 0.012	4.925 ± 0.274	3.133 ± .0.177
DenseNet10	203	0.907 ± 0.009	3.367 ± 0.177	0.892 ± 0.004	4.933 ± 0.091	3.053 ± 0.056

Data are the mean ± standard deviation. R² = coefficient of determination, RMSE = root mean square error, and RPD = the ratio of performance to deviation.