Irrigation management zone delineation and optimal irrigation scheduling for center pivot irrigation systems

Abstract

In this study, a three-stage irrigation management zone delineation approach and a model-based scheduler are proposed to provide variable rate irrigation schedules for a 26.4-hectare field at the Alberta Irrigation Technology Centre in Lethbridge, Alberta, Canada. After an initial demarcation of the investigated field into quadrants according to the four different crops growing in the field, the k-means clustering approach is used to delineate each quadrant further into smaller irrigation management zones on the basis of soil hydraulic parameters, elevation of the field and the variable rate irrigation resolution of the center pivot system. For each management zone, a long short-term memory (LSTM) neural network model is then developed to characterize the soil water dynamics in the root zone. Based on the LSTM models, and based on the LSTM models, a mixed-integer model predictive controller (MPC) is designed to prescribe irrigation schedules for the various management zones in each quadrant to ensure optimal crop water uptake while minimizing water consumption and irrigation costs. The applicability of the proposed approach is illustrated in simulations. The results show that the proposed approach achieves 26% water savings compared to a triggered scheduling scheme.