A Data-Driven Real-Time Irrigation Control Method Based on Model Predictive Control

摘要

The efficiency of irrigation systems is critically important for reducing water consumption in agricultural production process, especially with water scarcity nowadays being more and more severe all over the world. Empirical irrigation that often leads to over-watering and results in low yield and water waste should be prevented and substituted by advanced automatic irrigation systems. In this work, we focus on the data-driven real-time irrigation control and propose a model predictive control (MPC)-based approach to achieve desired plant root-zone deficit level given variable precipitation and evapotranspiration as disturbance. To take future weather into irrigation decision making, specialized local weather prediction is realized for local irrigation spots where regional weather forecast is less reliable, and the formulation of a dynamic uncertainty set is introduced to account for prediction errors and used in robust MPC design. The proposed approach is evaluated through a real-world case study in which we demonstrate that the implementation of the data-driven realtime irrigation control system effectively facilitates the control of plant root-zone deficit level for local irrigation spots.