The below described solution estimates prospective environmental outlooks that stochastically influence irrigation system controls through artificial intelligence optimization of inter competitive mathematical models.Synchronic consideration of multiple evaluation aspects allows to control sluices, pumps and other field elements of irrigation systems along the best optimal estimations and water demand.Estimations are calculated on the basis of own sensor data of the control system. Actively controllable system elements are set up on the basis of the balance of expected water wastage and proposed water usage. Each and every mathematical model variant competing with each other for control authorization, that are involved in the optimization of control system, is calculated in every case. Numerous model indicators are calculated based on certain model variant estimations and exposed data.Along the every model is uniform in different ways analogy (vö. Pitlik, 2006-2009, My-X projekt, INNOCSEKK-program) the most sustainable model will be the basis of calculations in an actual control decision. The above described ideal-based control after adequate amount of experience switches to function-optimization learning process, confronting model attributes with experimental results in a case of optional number of model variants, from which authorisation will be given according to the above described analogy, where the ideal-searcher model itself stays active among competitor models.
