Water resource management is critical for sustainable agriculture, especially in regions like Kazakhstan that face significant water scarcity challenges. This paper presents the development of a real-time water-level monitoring system designed to optimize water use in agriculture. The system integrates IoT sensors and cloud technologies, and analyzes data on water levels, temperature, humidity, and other environmental parameters. The architecture comprises a data collection layer with solar-powered sensors, a network layer for data transmission, a storage and integration layer for data management, a data processing layer for analysis and forecasting, and a user interface for visualization and interaction. The system was tested at the Left Bypass Canal in Taraz, Kazakhstan, demonstrating its effectiveness in providing real-time data for informed decision-making. The results indicate that the system significantly improves water use efficiency, reduces non-productive losses, and supports sustainable agricultural practices.

The paper analyzes the methods of stabilizing generation schedules of photovoltaic stations (PV) in electric power systems (EPS) in the process of balancing electricity. Since PV is characterized by the instability of electricity production due to dependence on weather conditions, an automatic system for forecasting their generation schedules (ASFG) for the next day has been created to increase the energy efficiency of PV. The process of automating the prediction of the power stations as part of the balancing group of the power stations and the algorithm for adjusting the prediction of power plant generation are considered. The criterion for managing the forecasting process is the minimization of the difference between the values ​ of forecasted and actual generation for the same period of time. Checking the performance and tuning of the ASFG PV in order to evaluate its functioning and the effectiveness of its application in the task of balancing the states of the EPS is possible only by means of simulation. It is shown that based on the nature of the process of forecasting the generation of PV using ASFG, it is advisable to use simulation modeling. Since the actual value of generation is constantly monitored during balancing using ASFG, it is possible to use these values ​​during simulation and proceed to real-time simulation. In this case, modeling is considered as an experimental method of research, according to which it is not the object itself that is subjected to perturbations and research, but the software-implemented computer model of the object. The real-life simulation model of the operation of the PV makes it possible to more fully consider the various modes of their operation in the process of balancing the modes of the EPS as part of the balancing group and to more reasonably choose decisions regarding the participation of the PV in generation, taking into account weather conditions and the limitations of the system operators of transmission and distribution of electricity.

A hydraulic drive invariant to reversal loads with a counterbalance valve is developed. The dependencies of static, dynamic, and energy characteristics on the counterbalance valve design parameters are found. The design parameters of the counterbalance valve for a hydraulic drive invariant to reversal loads are optimized.

A new method of successive approximation ADC calibration without interruption of the main conversion process is proposed. The method is based on the use of information redundancy in the form of redundant positional number systems. The method is based on the selection and analysis of unused combinations in the redundant ADC conversion characteristic.

Efficient water resource management is critically important for arid regions such as southern Kazakhstan. This paper presents a hybrid Internet of Things (IoT) and Vision-based Internet of Things (VIoT) architecture for real-time monitoring of water levels in irrigation channels. The proposed system integrates an ultrasonic water-level sensor, an IP camera with edge-based computer vision processing on a Raspberry Pi, wireless communication, an autonomous solar power supply, and discharge estimation using Manning’s equation. The VIoT subsystem applies image processing techniques, including gauge calibration, Canny edge detection, and pixel-to-metric conversion, to automatically estimate water level from captured video frames. Water-level measurements obtained from IoT sensors and video-based analysis are combined through synchronised data fusion to improve monitoring accuracy and reliability. The hybrid approach leverages the complementary strengths of IoT and VIoT by combining continuous quantitative sensing with visual verification capabilities. Field experiments conducted on the Merke River in the Zhambyl region of Kazakhstan over a 14-day observation period demonstrated stable real-time operation with RMSE = 0.311 cm, MAE = 0.279 cm, and Pearson r = 0.99 between the ultrasonic sensor and the vision-based estimates. Sensitivity analysis indicated that water level is the most influential parameter in Manning-based discharge estimation, confirming the importance of accurate level detection. The proposed system improves reliability by cross-checking independent data sources, making it applicable to monitoring water levels in agricultural regions.

The article provides an analysis of the physical processes underlying the operation of the measuring transducer, with a time based information presentation. A mathematical model is developed that describes the process of free oscillation attenuation excited in the LC-contour of primary measuring transducer, and analyzes and evaluates the influence of external factors that influence the measurement results. The ways of elimination of their influence on the results of measuring control are offered.