Feature selection-enhanced deep neural network for non-contact salinity estimation using microwave sensing: a performance comparison

Abstract Salinity is a key indicator in environmental, agricultural, and marine systems, requiring accurate, real-time monitoring to support sustainable resource management. While traditional measurement methods offer many advantages, they are impractical due to preparation requirements, contact-based operation, and sensitivity to environmental factors. In contrast, microwave sensing has emerged as a promising alternative for salinity detection, providing contactless operation, robustness, and sensitivity across wide frequency bands. In this study, we present a non-contact microwave sensing system integrated with a deep neural network (DNN) model for high-precision salinity prediction in water samples. The system, based on two Vivaldi antennas and PA6 plates matching layer, collects reflection and transmission coefficients (RCs and TCs) from water samples with salinity ranging from 0 to 30 000 ppm in increments of 1000 ppm. A dataset of 620 measurements was acquired at 751 frequency points (6759 features) and was used to train and test the DNN model. Furthermore, four different feature selection methods, T-test, Lasso-L1, mutual information (MI), and correlation analysis (CA), were employed to improve predictive accuracy. When the CA method was applied to the full frequency band (0.5–8 GHz), the mean absolute error (MAE) decreased from 299.91 ppm to 119.57 ppm while the coefficient of determination ( R 2 ) increased from 0.998 632 to 0.999 674, as the feature size was reduced by half. In addition, multiple narrow frequency bands were analyzed to identify optimal bands, and the CA method selected 680 features in the 0.5–2 GHz range, resulting in an MAE of 106.27 ppm and an R 2 of 0.999 702. In addition, when the MI method was applied only to the TC, the best performance was achieved in the 0.5–2.5 GHz band, yielding an MAE of 126.37 ppm and an R 2 : 0.999 695, while utilizing only 302 features.