A novel approach to soil nutrients prediction model for Bezuidenhout Park, Johannesburg, Gauteng Province, South Africa: attention temporal neural networks (ATNN).

The agricultural sector is moving from the Agriculture 4.0 model to the Agriculture 5.0 model because of advances in machine learning (ML), big data, and remote sensing. The purpose of this study is to introduce and validate a novel Attention Temporal Neural Network (ATNN) framework for the high-resolution prediction of soil nutrient concentrations, demonstrating its practical value using a real-world Digital Soil Mapping (DSM) dataset from Bezuidenhout Park, Johannesburg. The ATNN framework explicitly models temporal and contextual dependencies in multisource predictors, which include a Digital Elevation Model (DEM), spectral indices from Landsat and Sentinel-2 imagery, and meteorological covariates. The method involves combining the deep feature extraction capabilities of the ATNN with the strength of gradient-boosting regressors, specifically XGBoost, to leverage both architectures for robust tabular regression. The resulting ATNN-XGBoost hybrid model delivered the best performance in the experiments. It significantly reduced prediction error and improved agreement with laboratory measurements, achieving, for example, an RMSE ≈ 1.98 ppm, MAPE ≈ 2.81%, CCC ≈ of 0.76, and R