Since industrialization, world population is constantly on the rise, and so the demand for nutritious and healthy food is increasing as well. Traditional agriculture is steadily extended with and substituted by crop growing in greenhouses, which has better yield, more resistance against weather extremes, and overall more control over the growing parameters than regular agriculture. Greenhouses are traditionally controlled by expert growers, but currently there are not enough experienced growers in the world, and one grower can only handle a limited number of greenhouses. Autonomous greenhouse control solves this problem by taking over the task of defining setpoints for the low-level climate controllers, so that a full crop cycle can be managed with little effort from the grower’s part. Autonomous climate control in greenhouses can be extended by automatic irrigation, which reduces the workload manual calculation of irrigation decisions puts on the growers. Furthermore, it has the possibility of providing better yield than manual control during a crop cycle while reducing the water usage. This thesis introduces a new predictive irrigation control approach, which uses forecast weather data and climate predictions to create irrigation decisions with the use of a Mixed Logical Dynamical (MLD) Model Predictive Control (MPC) algorithm. The behaviour of the controller can be changed through costs and constraints, which can be defined according to the need of greenhouse growers. The water balance model, constituting from a simple Plant Water Uptake (PWU) model from literature as well as a novel drain model, is used to predict the water content of the growing substrate accurately for 24 hours ahead. The MLD MPC exploits the structure of nonlinearities inside the water balance model to create a Mixed Integer Linear Programming (MILP) optimal control problem, which can be solved using efficient algorithms with guarantee on optimality. The validation results on data of multiple greenhouses show, that the created algorithm can be generalized with little effort. The effectiveness of the control algorithm in open-loop was inspected through simulations.