Comparative evaluation of traditional, machine learning, and deep learning models for estimation of key soil properties using pedotransfer functions in the Indo‐Gangetic Plains

Direct measurement of soil hydro‐physical properties in the Indo‐Gangetic Plains (IGP) is labor‐intensive and costly, limiting site‐specific water management. This study addresses this gap by developing and validating region‐specific pedotransfer functions (PTFs) for six key properties: field capacity (FC), permanent wilting point (PWP), bulk density (BD), total available water (TAW), total porosity (Φ), and air capacity (AC) using advanced machine learning (ML) and deep learning (DL) approaches. A dataset of 351 soil samples collected from 117 georeferenced sites in the mid‐IGP loamy alluvial soils was used to construct PTFs based on sand, silt, clay, and soil organic carbon (SOC) as predictors. We compared traditional stepwise multiple linear regression (SMLR), ML algorithms (ridge regression, support vector regression, random forest, and XGBoost), and a DL‐based artificial neural network (ANN), and their performance was benchmarked against widely used published literature on PTFs. Results showed that advanced ML and DL models substantially outperformed both SMLR and existing PTFs, with ANN achieving the highest accuracy for FC ( R 2 = 0.84; root mean squared error [RMSE] = 0.025), TAW ( R 2 = 0.86; RMSE = 0.024), and AC ( R 2 = 0.86; RMSE = 0.021), XGBoost excelling for BD ( R 2 = 0.86; RMSE = 0.020) and Φ ( R 2 = 0.82; RMSE = 0.008). Feature importance analysis identified clay, SOC, and sand as the dominant predictors, though their influence varied across soil properties. This study provides the first robust, locally calibrated PTFs for multiple soil hydro‐physical properties in the mid‐IGP, offering accurate and scalable tools to improve irrigation scheduling, enhance water management, and strengthen sustainable agricultural practices in this critical food‐producing region.