Model Predictive Control for Real-Time Irrigation Scheduling

Irrigation underpins agricultural productivity. The purpose of irrigation is to match water supply to crop water demand. The effectiveness of irrigation depends on the quality of the timing and duration of watering events, also called irrigation scheduling. Most farmers use heuristic rules to determine irrigation events. This often leads to over-watering which results in lower crop yields and wasted water. By acquiring good estimates of a plant's water demand and local weather, it is possible to use optimization theory to compute an irrigation schedule that matches supply and demand thereby improving crop yields. Previous work has focused on scheduling irrigation over long time frames such as seasonal water allocations. Real-time irrigation scheduling, e.g. hourly or daily, has received little attention. Farmers rely on heuristic approaches implemented using simple spreadsheet tools to help them in this task. This approach cannot deal effectively with operational constraints and thereby results in poor performance. In this paper we develop a Model Predictive Control framework for real-time irrigation scheduling. The proposed formulation can take into account common operational constraints, including limitations on water availability as well as practical limits on the maximum or minimum amount of water that should be applied. We use measured climate data coupled with a simulation model to evaluate the proposed algorithm.