The article proposes a methodology for optimizing the process of irrigation of crops using a phytoindication system based on computer vision methods. We have proposed an algorithm and developed a system for obtaining a map of irrigation for maize in low latency mode. The system can be installed on a center pivot irrigation and consists of 8 IP cameras connected to a DVR connected to a laptop. The algorithm consists of three stages. Image preprocessing stage -applying an integrated excess green and excess red difference (ExGR) index. The classification stage is the application of the method that we choose depending on the operating conditions of the system. At the final stage, a neural network trained using the Resilient Propagation method is used, which determines the rate of watering of plants in the current sector of the location of the sprinkler. The selected methods of pretreatment and classification made it possible to achieve an accuracy of plant identification up to 93%, growth stages -up to 92% (with unconsolidated maize sowing and good lighting). System performance up to 100 plants in one second, which exceeds the performance of similar systems. The neural network showed an accuracy of 92% on the training set and 87% on the test set. Dynamic analysis of spatial and temporal variability leads to an increase in productivity and efficiency of water use. In addition, given the ubiquitous distribution of agribusiness management systems, this approach is quite simple to implement in the farm's conditions.

The article presents the results of studies of the operational efficiency of circular irrigation machines based on models of neural network irrigation control. Existing irrigation machines are not fully able to realize their advantages in irrigation due to the high degree of energy intensity. Traditional approaches based only on physical modeling of technical processes and relationships often make it difficult to find effective solutions. Intelligent irrigation control is essential for maximum efficiency and productivity. An approach based on a model of data mining is proposed, namely, control of a sprinkler using a neurocontroller. Most irrigation systems use ON / OFF controllers. These controllers cannot give optimal results for different time delays and different system parameters. The proposed controller based on an artificial neural network was created using MATLAB. The main modeling parameters are water pressure and speed. Neurocontrol, leads to the possible implementation of better and more effective management of irrigation machines.

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.

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.