This work proposes a stochastic model predictive control for an irrigation canal with uncertainties where a moving robot takes measurements across the canal considering criteria such as the robot’s velocity, energy consumption, and distances between the measuring spots. Tightened constraints are applied over the prediction horizon to the optimization so that the controller selects the optimal route for the robot from a control viewpoint. The simulations compare three different approaches, demonstrating that the proposed technique achieves superior results by reducing constraints violations and operational costs and ensuring more precise and reliable water level management across the canal compared to other methods.@en