Hybrid neural networks for improved crop yield prediction and water demand estimation

Purpose
Efficient water management is a key factor for agriculture in India. Particularly, it is aiding in the estimation of crop water requirements (CWR) to enhance yields and profitability. This study aims to improve crop water demand forecasting through a neural network-based time series model tailored for agricultural applications.


Design/methodology/approach
A hybrid forecasting model is proposed which combines an Ensemble of Hyperparameter-Tuned Nonlinear Autoregression Neural Network (E-NLARNN) for crop yield prediction with a Generalized Regression Neural Network (GRNN) for prediction error correction. E-NLARNN aggregates forecasts from multiple optimized NLARNNs to improve robustness in crop yield prediction. It combines prediction from multiple hyperparameter-tuned NLARNNs. GRNN models the residual errors to further refine predictions. Five different crop yield datasets were used to demonstrate the effectiveness of the proposed work. Its performance was benchmarked against traditional NLAR and E-NLARNN models using RMSE and R-value metrics.


Findings
This hybrid model demonstrated significant improvements, achieving RMSE reductions between 9.6% and 19.5% compared to E-NLARNN models and between 19.3% and 30.5% compared to the best TA-NLARNN variants across datasets. It consistently outperformed baseline methods in terms of accuracy and stability.


Originality/value
This study presents a novel hybrid neural network approach that integrates ensemble learning and regression-based error correction for agricultural time series forecasting. By enhancing prediction accuracy and offering interpretable insights, the proposed model supports more reliable irrigation planning and sustainable water resource management.