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.

Based on the most significant features of the angular velocity dynamic measurements selected by the authors, the main phases of measuring information transformation were established, which allowed to obtain new mathematical models in the form of transformation function, equations for estimating quantization errors, analytical dependences for measuring range that are initial for modeling physical processes occurring in such digital measuring channels with microprocessor control. The process of converting an analog quantity into a binary code is analytically described, an equation for estimating the absolute and relative quantization error is obtained and a measurement range is established, which provides a normalized value of relative quantization error for angular velocity measuring channels with encoder. For the first time, the equation of sampling error was obtained, and it was proved that the limiting factor of the angular velocity measurements upper limit is not only the normalized value of quantization error, as previously thought, but also the value of sampling frequency fD. Therefore, to expand the measurement range (by increasing the upper limit of measurement), it is proposed not only to increase the speed of analog-to-digital conversion hardware, but also to reduce the execution time of software drivers for transmitting measurement information to RAM of microprocessor system. For this purpose, the analytical dependences of estimating the upper limit of measurement based on the value of the sampling step for different modes of measurement information transmission are obtained. The practical implementation of the software mode measurement information transmission is characterized by a minimum of hardware costs and maximum execution time of the software driver, which explains its low speed, and therefore provides a minimum value of the upper limit measurement. In the interrupt mode, the upper limit value of the angular velocity measurement is higher than in the program mode due to the reduction of the software driver’s execution time (tFl = 0). The maximum value of the angular velocity measurements upper limit can be achieved using the measurement information transmission in the mode of direct access to memory (DMA) by providing maximum speed in this mode (tFl = 0, tDR = 0). In addition, the application of the results obtained in the work allows at the design stage (during physical and mathematical modeling) to assess the basic metrological characteristics of the measuring channel, aimed at reducing the development time and debugging of hardware, software, and standardization of their metrological characteristics.

Currently, there is an avalanche-like increase in the need for automatic text processing, respectively, new effective methods and tools for processing texts in natural language are emerging. Although these methods, tools and resources are mostly presented on the internet, many of them remain inaccessible to developers, since they are not systematized, distributed in various directories or on separate sites of both humanitarian and technical orientation. All this greatly complicates their search and practical use in conducting research in computational linguistics and developing applied systems for natural text processing. This paper is aimed at solving the need described above. The paper goal is to develop model of an intelligent information resource based on modern methods of natural language processing (IIR NLP). The main goal of IIR NLP is to render convenient valuable access for specialists in the field of computational linguistics. The originality of our proposed approach is that the developed ontology of the subject area “NLP” will be used to systematize all the above knowledge, data, information resources and organize meaningful access to them, and semantic web standards and technology tools will be used as a software basis.

Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it.

The paper presents the results of analytical modeling of the case of the presence zone of an abstract object characterized by a solid mass. It has several zones of presence based on the foundations of the TONTOR theory. Research determined that the discrete solid-state zone of the presence characterizes the solid part of the AE itself or the particles that form the surrounding space near the abstract entity and is the most powerful zone among the existing zones. The proposed model for determining the parameters of TONTOR zones of an object provides the possibility of analyzing the state of this object during its movements in the working space and metrological measurements of coordinates. These metrological aspects in the automatic mode of operation of object state analysis system determine the properties that increase the accuracy and speed of operations for calculating object movement trajectories in various fields of research.

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.