Precision farming is one of the ways of transition to the intensive methods of agricultural production. The case of application of unmanned aerial vehicles (UAVs) for solving problems of agriculture and animal husbandry is among the actively studied issues. The UAV is capable of solving the tasks of monitoring, fertilizing, herbicides, etc. However, the effective use of UAV requires to solve the tasks of flight planning, taking into account the heterogeneity of the available attachments and the problem solved in the process of the overflight. This research investigates the problem of flight planning of a group of heterogeneous UAVs applied to solving the issues of coverage, which may arise both in the course of monitoring and in the process of the implementation of agrotechnical measures. The method of coverage path planning of heterogenic UAVs group based on a genetic algorithm is proposed; this method provides planning of flight by a group of UAVs using a moving ground platform on which UAVs are recharged and refueled (multi heterogenic UAVs coverage path planning with moving ground platform (mhCPPgp)). This method allows calculating a fly by to solve the task of covering fields of different shapes and permits selecting the optimal subset of UAVs from the available set of devices; it also provides a 10% reduction in the cost of a flyby compared to an algorithm that does not use heterogeneous UAVs or a moving platform.

Salinization of cultivated soil is an important negative factor that reduces crop yields. Obtaining accurate and timely data on the salinity of soil horizons allows for planning the agrotechnical measures to reduce this negative impact. The method of soil salinity mapping of the 0–30 cm layer on irrigated arable land with the help of multispectral data received from the UAV is described in this article. The research was carried out in the south of the Almaty region of Kazakhstan. In May 2022, 80 soil samples were taken from the ground survey, and overflight of two adjacent fields was performed. The flight was carried out using a UAV equipped with a multispectral camera. The data preprocessing method is proposed herein, and several machine learning algorithms are compared (XGBoost, LightGBM, random forest, support vector machines, ridge regression, elastic net, etc.). Machine learning methods provided regression reconstruction to predict the electrical conductivity of the 0–30 cm soil layer based on an optimized list of spectral indices. The XGB regressor model showed the best quality results: the coefficient of determination was 0.701, the mean-squared error was 0.508, and the mean absolute error was 0.514. A comparison with the results obtained based on Landsat 8 data using a similar model was performed. Soil salinity mapping using UAVs provides much better spatial detailing than satellite data and has the possibility of an arbitrary selection of the survey time, less dependence on the conditions of cloud cover, and a comparable degree of accuracy of estimates.

The media plays an important role in disseminating facts and knowledge to the public at critical times, and the COVID-19 pandemic is a good example of such a period. This research is devoted to performing a comparative analysis of the representation of topics connected with the pandemic in the internet media of Kazakhstan and the Russian Federation. The main goal of the research is to propose a method that would make it possible to analyze the correlation between mass media dynamic indicators and the World Health Organization COVID-19 data. In order to solve the task, three approaches related to the representation of mass media dynamics in numerical form—automatically obtained topics, average sentiment, and dynamic indicators—were proposed and applied according to a manually selected list of search queries. The results of the analysis indicate similarities and differences in the ways in which the epidemiological situation is reflected in publications in Russia and in Kazakhstan. In particular, the publication activity in both countries correlates with the absolute indicators, such as the daily number of new infections, and the daily number of deaths. However, mass media tend to ignore the positive rate of confirmed cases and the virus reproduction rate. If we consider strictness of quarantine measures, mass media in Russia show a rather high correlation, while in Kazakhstan, the correlation is much lower. Analysis of search queries revealed that in Kazakhstan the problem of fake news and disinformation is more acute during periods of deterioration of the epidemiological situation, when the level of crime and poverty increase. The novelty of this work is the proposal and implementation of a method that allows the performing of a comparative analysis of objective COVID-19 statistics and several mass media indicators. In addition, it is the first time that such a comparative analysis, between different countries, has been performed on a corpus in a language other than English.

The paper presents the results of a correlation analysis between the information trends in the electronic media of Kazakhstan and indicators of the epidemiological situation of COVID-19 according to the World Health Organization (WHO). The developed method is based on topic modeling and some other methods of processing natural language texts. The method allows for calculating the correlations between media topics, moods, the results of full-text search queries, and objective WHO data. The analysis of the results shows how the attitudes of society towards the problems of COVID-19 changed from 2021–2022. Firstly, the results reflect a steady trend of decreasing interest of electronic media in the topic of the pandemic, although to an unequal extent for different thematic groups. Secondly, there has been a tendency to shift the focus of attention to more pragmatic issues, such as remote learning problems, remote work, the impact of quarantine restrictions on the economy, etc.