Integration of Machine Learning and VIRS for Soil Potassium Content Prediction

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ABSTRACT At present, the combination of machine learning and visible and infrared spectroscopy (VIRS) stands out as one of the most promising methods for predicting soil potassium content. In this study, a total of 100 soil samples were collected in the vicinity of Xunsi River, Hubei University of Technology. Spectral information and corresponding soil potassium content were obtained for these 100 soil samples. Subsequently, the original spectra of the soil samples were denoised using the locally weighted regression (loess) method. Feature wavelength selection was performed using ∆Gini and Successive Projections Algorithm (SPA) for dimensionality reduction. The model was then optimized by adjusting its parameters. Finally, random forest (RF), back propagation (BP), Genetic algorithm-back propagation (GA-BP) and particle swarm optimization-back propagation (PSO-BP) prediction models were established to predict soil potassium content. The results show that the RF model exhibits superior prediction performance compared to other algorithms, with a coefficient of determination (R2) of 0.851 and a root mean square error (RMSE) of 0.503. The RF model accurately predicts soil potassium content and validates the stability of the VIRS method for rapid estimation. Among the BP model and its optimization variants, the PSO-BP-Levenberg-Marquardt model demonstrates the best predictive capability, yielding an R2 of 0.791 and an RMSE of 0.621. This combination algorithm holds potential as a valuable reference for the selection of optimization algorithms and training functions for the BP model within the research domain.