Autonomous real-time adaptive management of soil salinity using a receding horizon control algorithm: a pilot-scale...

Area of Science:

• Agricultural Engineering
• Environmental Science
• Soil Science

Background:

• Irrigation with reclaimed water can cause soil salinization, a significant agricultural challenge.
• Existing soil salinity control methods often lack real-time sensor feedback integration.
• Autonomous management systems are needed to optimize water reuse and agricultural productivity.

Purpose of the Study:

• To develop and test an autonomous control scheme for managing soil salinization using real-time sensor data.
• To integrate Receding Horizon Control (RHC) with soil observation technologies for proactive salinity management.
• To demonstrate the capability of RHC in maintaining specific soil salt levels during irrigation.

Main Methods:

• Utilized Receding Horizon Control (RHC), a feedback-control scheme incorporating real-time sensor measurements and predictive models.
• Employed a simulation model based on the Richards equation coupled with energy and solute transport equations as a state estimator.
• Integrated vertical multi-sensor arrays for continuous soil condition feedback and an optimization algorithm to adjust irrigation parameters.

Main Results:

• The Receding Horizon Control (RHC) scheme autonomously managed soil salinization by manipulating irrigation rates and frequency.
• A small-scale field test confirmed the RHC system's ability to maintain specified salt levels at target soil depths.
• The study demonstrated successful integration of sensor networks and optimization algorithms for autonomous control.

Conclusions:

• Receding Horizon Control (RHC) offers a viable solution for autonomous soil salinization management in irrigated agriculture.
• Real-time sensor feedback and predictive modeling are crucial for effective salinity control.
• This approach supports sustainable water reuse and agricultural goals by mitigating soil salinization risks.