Nonlinear Regression Based Deep Radial Basis Function and Advanced Image Processing in Soil Estimation

Abstract

Agricultural planning, environmental management, and soil estimation all depend on exact prediction of soil properties. Conventional methods find it challenging to manage the complex, nonlinear connections between soil parameters and visual characteristics. This work handles this challenge using advanced image processing techniques along with a deep radial basis function (RBF) network for nonlinear regression. The deep RBF network detects complicated nonlinear correlations in soil data by using numerous hidden layers with radial basis functions. From remote sensing images, image processing methods enhance the feature extraction process thereby enhancing the accuracy of soil property forecasts. Experimental data shows that the proposed method beats more conventional linear regression models rather significantly. Against an MSE of 0.056 and R² of 0.76 for linear models, the deep RBF model especially obtained a mean squared error (MSE) of 0.032 and a coefficient of determination (R²) of 0.87. These results show how effectively nonlinear regression estimates dirt combined with contemporary image processing.