TY  - JOUR
AU  - Effiom, Samuel Oliver
AU  - Anyin, Paul Betiang
AU  - Ugbong, Emmanuel Akomaye
PY  - 2026
DA  - 2026/08/24
TI  - Model-Based Design and Evaluation of a Multi-Variable Hybrid Predictive Control Loop for Precision Fertigation Using Multi-Sensor Data Fusion
JO  - Digital Intelligence in Agriculture
T2  - Digital Intelligence in Agriculture
JF  - Digital Intelligence in Agriculture
VL  - 2
IS  - 2
SP  - 114
EP  - 125
DO  - 10.62762/DIA.2026.947445
UR  - https://www.icck.org/article/abs/DIA.2026.947445
KW  - precision fertigation
KW  - hybrid predictive control
KW  - multi-sensor data fusion
KW  - extended Kalman filter
KW  - mixed-integer optimization
AB  - 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.
SN  - 3069-3187
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Effiom2026ModelBased,
  author = {Samuel Oliver Effiom and Paul Betiang Anyin and Emmanuel Akomaye Ugbong},
  title = {Model-Based Design and Evaluation of a Multi-Variable Hybrid Predictive Control Loop for Precision Fertigation Using Multi-Sensor Data Fusion},
  journal = {Digital Intelligence in Agriculture},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {114-125},
  doi = {10.62762/DIA.2026.947445},
  url = {https://www.icck.org/article/abs/DIA.2026.947445},
  abstract = {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.},
  keywords = {precision fertigation, hybrid predictive control, multi-sensor data fusion, extended Kalman filter, mixed-integer optimization},
  issn = {3069-3187},
  publisher = {Institute of Central Computation and Knowledge}
}