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  1. Home
  2. Towards A Modelling, Optimization And Predictive Control Framework For Smart Irrigation.
  1. Home
  2. Towards A Modelling, Optimization And Predictive Control Framework For Smart Irrigation.

Related Experiment Video

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Towards a modelling, optimization and predictive control framework for smart irrigation.

Erion Bwambale1,2, Felix K Abagale2,3, Geophrey K Anornu4

  • 1Department of Agricultural and Biosystems Engineering, Makerere University, P. O. Box 7062, Kampala, Uganda.

Heliyon
|October 4, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Model predictive control (MPC) optimizes smart irrigation scheduling for arid lands. This advanced system boosts crop yield and water use efficiency (WUE) while significantly saving water compared to traditional methods.

Keywords:
Data-driven modellingDynamics and controlModel predictive controlSmart irrigationSystem identificationWater use efficiency

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Area of Science:

  • Agricultural Engineering
  • Control Systems
  • Water Resource Management

Background:

  • Smart irrigation scheduling is crucial for sustainable agriculture, particularly in water-scarce arid and semi-arid regions.
  • Optimizing water use in agriculture is essential to address global water scarcity.
  • Existing irrigation control methods may not fully leverage predictive capabilities for maximum efficiency.

Purpose of the Study:

  • To design and simulate a model predictive controller (MPC) for smart irrigation scheduling.
  • To evaluate the impact of MPC on crop yield, water use efficiency (WUE), and water savings in tomato production.
  • To compare the performance of MPC against manual and open-loop control strategies.

Main Methods:

  • Development of a mathematical model for the irrigation system to predict future states.
  • Simulation of three control strategies: manual control, open-loop control, and model predictive control (MPC).
  • Comparison of strategies using a Completely Randomized Design (CRD) methodology for tomato production.
  • Main Results:

    • Model predictive control (MPC) achieved the highest average crop yield (20 t/ha) and superior water use efficiency (WUE) (10.4 kg/m³).
    • MPC demonstrated significant water savings, reducing consumption by 29% compared to manual control and 8% compared to open-loop control.
    • MPC outperformed manual and open-loop control in optimizing irrigation scheduling for enhanced agricultural outcomes.

    Conclusions:

    • Model predictive control (MPC) is highly effective for smart irrigation scheduling, optimizing crop yield and water conservation.
    • The proposed MPC framework offers a sustainable solution for agriculture in arid and semi-arid lands, addressing water scarcity.
    • MPC technology has the potential to significantly contribute to global food security and sustainable water resource management.