February 1, 2022 | Agricultural Water Management |
Introduction: This review, conducted by researchers from the University for Development Studies and Kwame Nkrumah University of Science and Technology in Ghana, examines the current state of smart irrigation monitoring approaches, including soil-based, weather-based, and plant-based methods. It further compares open-loop and closed-loop control strategies to identify the most suitable combinations for open-field applications.
Key findings: The review finds that water use efficiency improves most effectively when monitoring and control are integrated rather than managed separately. Closed-loop control systems consistently outperform open-loop alternatives, as open-loop systems cannot respond to real-world uncertainties in soil moisture conditions and crop responses. Among monitoring strategies, soil moisture sensors achieve water savings of 50–58.8%, depending on crop type and system configuration. Weather-based scheduling using evapotranspiration models achieves approximately 30% water savings in cantaloupe and greenhouse conditions, while plant-based approaches using the CWSI and NDVI indicators generate savings of 10–45%.
Among control strategies, Model Predictive Control (MPC) shows the strongest overall performance. However, most MPC studies remain at the simulation stage, with limited validation under real open-field conditions. Artificial neural networks (ANNs) reduce energy and water use by 20.5–23.9% in strawberry cultivation. The review recommends integrating soil, plant, and weather monitoring inputs within a discrete MPC framework for open-field applications. Key limitations include the lack of open-field dynamic models capable of handling uncertainties from soil variability and weather disturbances. Future research should prioritize the development and field evaluation of hybrid MPC approaches in real open-field environments.
Figure | Classification of irrigation control strategies.
