Application of swarm intelligence algorithms to the characteristic wavelength selection of soil moisture content

Swarm intelligence algorithms own superior performance in solving high-dimensional and multi-objective optimization problems. The application of the swarm intelligence algorithms to visible and near-infrared (VIS-NIR) spectral analysis of soil moisture can contribute to the optimization of the soil moisture prediction model and the development of the real-time soil moisture sensor. In this study, a high-resolution spectrometer was used to obtain spectral data of different levels of soil moisture which were manually configured. Isolation Forest algorithm (iForest) was used to eliminate outliers from the data. Based on the root mean square error of prediction RMSEP of Back Propagation Neural Network (BPNN) model results, a series of new swarm intelligence algorithms, including Manta Ray Foraging Optimization (MRFO), Slime Mould Algorithm (SMA), etc., were used to select the characteristic wavelengths of soil moisture. The analysis results showed that MRFO owned the best performance if only from the predictive capability perspective and SMA had a better performance when considering the proportion of the selecting wavelengths and the results of the model prediction. By comparing and analyzing the modeling results of traditional intelligence algorithms Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), it was found that the new swarm intelligence had a better performance in selecting the characteristic wavelengths of soil moisture. Integrating the results of all intelligence algorithms used, soil moisture sensitive wavelengths were selected as 490 nm, 513 nm, 543 nm, 900 nm and 926 nm, which provide the basis for the design of real-time soil moisture sensor based on VIS-NIR.