Evaluation of soil texture classification from orthodox interpolation and machine learning techniques

The current investigation examines the effectiveness of various approaches in predicting the soil texture class (clay, silt, and sand contents) of the Rawalpindi district, Punjab province, Pakistan. The employed techniques included artificial neural networks (ANNs), kriging, co-kriging, and inverse distance weighting (IDW). A total of 44 soil specimens from depths of 10-15 cm were gathered, and then the hydrometer method was adopted to measure their texture. The map of soil grain sets was formulated in the ArcGIS environment, utilizing distinct interpolation approaches. The MATLAB software was used to evaluate soil texture. The gradient fraction, latitude and longitude, elevation, and soil texture fragments of points were proposed to an ANN. Several statistical values, such as correlation coefficient (R), geometric mean error ratios (GMER), and root mean square error (RMSE), were utilized to evaluate the precision of the intended techniques. In assessing grain size and spatial dissemination of clay, silt, and sand, the effectiveness and precision of ANN were superior compared to kriging, cokriging, and inverse distance weighting. Still, less than a 50% correlation was observed using the ANN. In this examination, the IDW had inferior precision compared to the other approaches. The results demonstrated that the practices produced acceptable results and can be used for future research. Soil texture is among the most central variables that can manipulate agriculture plans. The prepared maps exhibiting the soil texture groups are imperative for crop yield and pastoral scheduling.