article · Digital Intelligence in Agriculture
Conventional agricultural fertigation applies water and nutrients uniformly, overlooking soil root-zone variability and causing resource waste and groundwater contamination. To resolve this, a multi-variable hybrid predictive control framework was designed to automate precision fertigation. The system couples continuous physical soil dynamics with discrete operational controls, such as pump schedules and fertiliser injection. It incorporates an Extended Kalman Filter to fuse data from capacitive moisture and electrical conductivity sensors, addressing sensor drift and noise through adaptive covariance estimation. In a 30-day closed-loop simulation, the controller demonstrated robust tracking and disturbance rejection. It paused operations during rainfall to leverage natural precipitation and prevent nutrient leaching. Overall, the approach decreased water consumption by 25 percent and fertiliser use by 30 percent compared to conventional uniform fertigation methods.
Rising fertiliser costs and freshwater scarcity present severe challenges to farming communities worldwide. Uniform irrigation and fertiliser application often lead to wasteful runoff and chemical leaching into local groundwater. Demonstrating automated control systems that dynamically adjust to real-time soil conditions and weather events offers a practical method to preserve vital water resources, cut agricultural input costs, and protect surrounding ecosystems.
This control framework could eventually be integrated into automated agricultural management systems and smart irrigation controllers used by commercial farms and greenhouse operators. However, the system currently sits at an early stage of development, as the reported performance is based on a 30-day closed-loop simulation in MATLAB rather than physical deployment in field conditions with operational hardware.
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Efficient water and nutrient management remains a major challenge in modern agriculture due to rising fertilizer costs, global water scarcity, and environmental concerns such as groundwater contamination from nutrient leaching. Conventional fertigation typically relies on uniform application rates that neglect spatial and temporal root-zone variability, resulting in inefficient resource utilization. To address these limitations, this study proposes and evaluates an automated Multi-Variable Hybrid Predictive Control (HPC) framework for precision fertigation. The system captures the highly nonlinear dynamics of rapid soil moisture changes and slower nutrient transport using a multi-variable state-space model that combines continuous physical processes with discrete control logic, including pump scheduling and fertilizer injection. The predictive controller is enhanced by an Extended Kalman Filter (EKF)-based Multi-Sensor Data Fusion (MSDF) framework, which mitigates measurement noise and sensor drift from capacitive moisture and electrical conductivity (EC) sensors through adaptive covariance estimation, providing reliable root-zone state estimates. A 30-day closed-loop MATLAB simulation demonstrates robust tracking performance and disturbance rejection. Following the receding horizon strategy, the controller suspended irrigation and fertilization during rainfall events, effectively utilizing natural precipitation and eliminating the risk of groundwater nutrient leaching during precipitation events. Compared with conventional uniform fertigation, the proposed approach achieved a 25% reduction in water use and a 30% reduction in fertilizer consumption. Overall, the proposed framework provides an effective solution for improving resource-use efficiency while supporting environmentally sustainable precision agriculture.
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DOI: 10.62762/dia.2026.947445
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