Knowledge-based optimal irrigation scheduling of agro-hydrological systems

TL;DR

This paper presents a novel, model-based irrigation scheduling method for large, three-dimensional agro-hydrological systems that optimizes water use, crop yield, and electricity consumption by reducing system complexity and employing predictive control.

Contribution

It introduces a structure-preserving model reduction technique and an MPC-based scheduler for 3D agro-hydrological systems, addressing high dimensionality and weather disturbances.

Findings

The approach improves water conservation and crop yield.

It reduces electricity usage by optimizing irrigation timing.

The method outperforms traditional scheduling in multiple scenarios.

Abstract

The typical agricultural irrigation scheduler provides information on how much to irrigate and when to irrigate. The accurate and effective scheduler decision for a large agricultural field is still an open research problem. In this work, we address the high dimensionality of the agricultural field and propose a systematic approach to provide optimum irrigation amount and irrigation time for three-dimensional agro-hydrological systems. The water dynamics of the agro-hydrological system are represented using a cylindrical three-dimensional Richards Equation. We introduce a structure-preserving model reduction technique to decrease the dimension of the system model. Using the reduced model, the optimization-based closed-loop scheduler is designed in model predictive control (MPC) environment. The closed-loop approach can handle weather disturbances and provide improved yield and water conservation. The primary objective of the proposed scheduler is to ensure maximum yield, minimum water consumption and maximize the time between the two irrigation events, which results in less electricity usage. The proposed approach is applied to three different scenarios to show the effectiveness and superiority of the proposed framework.