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The development of well-devised irrigation scheduling methods is desirablefrom the perspectives of plant quality and water conservation. In this article,a model predictive control (MPC) with discrete actuators is developed forirrigation scheduling, where a long short-term memory (LSTM) model of thesoil-water-atmosphere system is used to evaluate the objective of ensuringoptimal water uptake in crops while minimizing total water consumption andirrigation costs. A heuristic method involving a sigmoid function is used inthis framework to enhance the computational efficiency of the scheduler. Thescheduling scheme is applied to homogeneous and spatially variable fields andthe results indicate that the LSTM-based MPC with discrete actuators is able toprescribe optimal or near-optimal irrigation schedules that are typical ofirrigation practice.

LSTM-based model predictive control with discrete inputs for irrigation scheduling