Results of researches of possibility and efficiency of introduction of intelligent control systems, namely neural network speed control, in control systems of water sprinklers of circular action are presented in this article. The size of an irrigation norm essentially depends on speed, and this dependence is not linear and is caused by many stochastic factors. The results of comparing the theoretical and actual values of "irrigation norm-rate" dependencies show their significant differences, which affects the quality of irrigation. Traditional approaches based only on physical IV International Scientific and Practical Conference "Modern S&T Equipments and Problems in Agriculture" 207 modelling of technical processes and connections often make it difficult to find effective solutions. Technological advances that increase data collection and analysis capabilities can significantly improve the efficiency of engineering solutions. An approach based on the model of intelligent data analysis, namely the model of neuro velocity control, is proposed. Neuro-control, leads to a possible implementation of better and more efficient management of sprinkling equipment.

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The article presents the development of an intelligent control system for experimental seed production, the program, the drawings necessary for explanation.

The article presents the results of modeling an intelligent control system for an irrigation complex. The introduction of precision irrigation technologies requires the development of new approaches to technical support. Traditional approaches based on simple process automation often do not lead to effective solutions. An approach based on the model of intellectualization of automated control systems is proposed. The structure of the intelligent control system for the irrigation complex is substantiated, which is based on an artificial neural network.

Optimizing water management for irrigated agriculture requires the development of modern approaches to determining and predicting water consumption, despite the large number of already developed models. The article presents approaches to neural network modeling of water consumption. The advantage of such modeling is high accuracy and ability to adapt to changing parameters of the model, which distinguishes them from traditional methods and allows you to provide optimal results in terms of minimizing errors and increasing the tightness of the relationship between variables.