Abstract
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 R2 ≈ of 0.69 for aluminium. This approach materially improved nutrient estimation accuracy over baseline models, including Random Forest (RF), Gradient Boosting (GB), and AdaBoost (ADB). The key contributions to this work are threefold: (1) the development of a compact ATNN architecture tailored for soil nutrient time series and spatial covariates; (2) a practical hybridisation strategy that pairs attention-based feature encoding with XGBoost (XGB); and (3) an empirical demonstration of superior performance on a real South African DSM dataset. These advances support more accurate and timely fertiliser management, offering a scalable path towards smarter, more sustainable precision agriculture systems.
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Abbreviations
- AI:
Artificial intelligence
- ACO:
Ant colony optimisation
- AdaBoost or ADB:
Adaptive boosting
- ATNN:
Attention temporal neural network
- API:
Application programming interface
- CCC:
Concordance correlation coefficient
- CP:
Correlation plot
- DEM:
Digital elevation model
- DT:
Decision tree
- DP:
Deep learning
- EDA:
Exploratory data analysis
- EA:
Evolutionary algorithm
- GB:
Gradient boost
- GIS:
Geographic information system
- IR:
Infrared ray
- LR:
Linear regression
- LST:
Land surface temperature
- MAE:
Mean absolute error
- MAPE:
Mean percentage error
- MIRAS:
Microwave imaging radiometer using aperture synthesis
- MSE:
Mean square error
- ML:
Machine learning
- NDVI:
Normalised difference vegetation index
- OLI:
Operational land imager
- OLS:
Ordinary least squares
- PCA:
Principal component analysis
- RF:
Random forest
- RGB:
Red–green–blue
- RMSE:
Root mean square error
- R2
: R-squared (coefficient of determination)
- SMOS:
Soil moisture and ocean salinity
- SOC:
Soil organic carbon
- SRTM:
Shuttle radar topography mission
- SWI:
Short-wave infrared
- TIRS:
Thermal infrared sensor
- UTC:
Coordinated universal time
- UV:
Ultraviolet
- XGB:
Extreme gradient boosting
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This research did not receive funding.
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Appendix I: ATNN architecture and training specifications
Appendix I: ATNN architecture and training specifications
Parameter | Value | Notes |
|---|---|---|
Model variant | Attention temporal neural network (ATNN) | Temporal encoder with attention mechanism |
Number of encoder layers | 4 | Stacked attention-based encoder blocks |
Hidden dimension (d_model) | 128 | Embedding and attention projection size |
Feed-forward dimension (d_ff) | 512 | Inner FFN layer size |
Number of attention heads | 4 | Multi-head self-attention |
Sequence length | 12 | Temporal timesteps per input sequence |
Dropout rate | 0.20 | Applied in attention and FFN layers |
Activation function | GELU | Non-linear activation in FFN |
Batch size | 64 | Mini-batch size |
Training epochs | 150 | With early stopping |
Optimiser | AdamW | Adaptive optimiser with weight decay |
Learning rate | 1e-4 | Initial learning rate |
Weight decay | 1e-5 | Regularisation parameter |
Learning rate scheduler | ReduceLROnPlateau | Factor = 0.5, patience = 5 |
Loss function | Mean squared error (MSE) | Regression loss |
Early stopping | Yes (patience = 15) | Validation-based stopping |
Gradient clipping | 1.0 | L2 norm clipping |
Random seed | 42 | Ensures reproducibility |
Data normalisation | StandardScaler | Fitted on training data only |
Train/validation/test split | 70% / 15% / 15% | Spatial block sampling |
Framework | PyTorch | Version ≥ 1.10 |
Hardware | Single GPU (≥ 16 GB VRAM) | CPU supported but slower |
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Dada, B.A., Nwulu, N.I. & Olukanmi, S.O. A novel approach to soil nutrients prediction model for Bezuidenhout Park, Johannesburg, Gauteng Province, South Africa: attention temporal neural networks (ATNN).
Sci Rep (2026). https://doi.org/10.1038/s41598-026-56150-z
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DOI: https://doi.org/10.1038/s41598-026-56150-z
Keywords
- Soil nutrient prediction
- Digital soil mapping
- ATNN-XGB
- Soil nutrient management
- Agriculture 5.0
- ML
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