«Development of Intelligent Data Processing and Flight Planning Models for Precision Farming Tasks Using UAVs» (IRN AP08856412)
The aim of the project
Development of models of data processing and flight planning of technically heterogeneous UAVs for the decision of problems of precision farming on the basis of methods of artificial intelligence.
Application of the UAV for solving a wide range of monitoring and control tasks in the sectors of management of Kazakhstan is limited not only by individual technical features of this mobile platform but also by insufficient development of practically applicable intelligent methods, algorithms, and systems of motion control and analysis of data coming from the UAV. Aim of the projects is the development of practically applicable methods, providing the solution of flight control tasks (including a group of vehicles), identification, and classification of objects of observation, with the help of modern methods of machine learning to solve the problems of precision agriculture. The expected results are also applicable in other branches of production for solving monitoring tasks.
The project will result in
Published at least 3 (three) articles and/or reviews in peer-reviewed scientific publications on the scientific direction of the project, included in 1 (first), 2 (second) or 3 (third) quartile in the Web of Science and (or) having a percentage of CiteScore in the Scopus database at least 50 (fifty);
Published at least 1 (one) article in a peer-reviewed foreign and/or domestic publication with non-zero impact factor (recommended by Committee on Control in Education and Science of the Ministry of Education and Science of the Republic of Kazakhstan);
Certificate of implementation received or recommendations on implementation developed;
The scientific and technical documentation for the software will be developed;
Certificates of intellectual property registration for software will be obtained.
Scientific results can be applied or commercialized as part of the final product in the tasks of management and monitoring in agriculture and other areas of economic application UAV
The first phase (tasks 1.1, 2.1, 3.1, 4.1, 5.1, 6.1) of the project was completed.
1. Ravil I. Mukhamediev , Adilkhan Symagulov , Yan Kuchin , Elena Zaitseva, Alma Bekbotayeva, Kirill Yakunin , Ilyas Assanov , Vitaly Levashenko, Yelena Popova , Assel Akzhalova, Sholpan Bastaubayeva and Laila Tabynbaeva. Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country //Applied Sciences. – 2021. – Т. 11. – №. 21. – С. 10171. https://doi.org/10.3390/app112110171(CiteScore highest quartile = Q2, JCR — Q2, CiteScore =3.0, CiteScore highest percentile=71%, WoS IF=2.679)
The use of unmanned aerial vehicles (UAVs) in various spheres of human activity is a promising direction for countries with very different types of economies. This statement refers to resource-rich economies as well. The peculiarities of such countries are associated with the dependence on resource prices since their economies present low diversification. Therefore, the employment of new technologies is one of the ways of increasing the sustainability of such economy development. In this context, the use of UAVs is a prospect direction, since they are relatively cheap, reliable, and their use does not require a high-tech background. The most common use of UAVs is associated with various types of monitoring tasks. In addition, UAVs can be used for organizing communication, search, cargo delivery, field processing, etc. Using additional elements of artificial intelligence (AI) together with UAVs helps to solve the problems in automatic or semi-automatic mode. Such UAV is named intelligent unmanned aerial vehicle technology (IUAVT), and its employment allows increasing the UAV-based technology efficiency. However, in order to adapt IUAVT in the sectors of economy, it is necessary to overcome a range of limitations. The research is devoted to the analysis of opportunities and obstacles to the adaptation of IUAVT in the economy. The possible economic effect is estimated for Kazakhstan as one of the resource-rich countries. The review consists of three main parts. The first part describes the IUAVT application areas and the tasks it can solve. The following areas of application are considered: precision agriculture, the hazardous geophysical processes monitoring, environmental pollution monitoring, exploration of minerals, wild animals monitoring, technical and engineering structures monitoring, and traffic monitoring. The economic potential is estimated by the areas of application of IUAVT in Kazakhstan. The second part contains the review of the technical, legal, and software-algorithmic limitations of IUAVT and modern approaches aimed at overcoming these limitations. The third part—discussion—comprises the consideration of the impact of these limitations and unsolved tasks of the IUAVT employment in the areas of activity under consideration, and assessment of the overall economic effect. View Full-Text
2. Mukhamediev RI, Symagulov A, Kuchin Y, Yakunin K, Yelis M. From Classical Machine Learning to Deep Neural Networks: A Simplified Scientometric Review//Applied Sciences. – 2021. – Т. 11. – №. 12. – P. 5541. — https://doi.org/10.3390/app11125541(CiteScore highest quartile = Q2, JCR — Q2, CiteScore =3.0, CiteScore highest percentile=71%, WoS IF=2.679).
There are promising prospects on the way to widespread use of AI, as well as problems that need to be overcome to adapt AI&ML technologies in industries. The paper systematizes the AI sections and calculates the dynamics of changes in the number of scientific articles in machine learning sections according to Google Scholar. The method of data acquisition and calculation of dynamic indicators of changes in publication activity is described: growth rate (D1) and acceleration of growth (D2) of scientific publications. Analysis of publication activity, in particular, showed a high interest in modern transformer models, the development of datasets for some industries, and a sharp increase in interest in methods of explainable machine learning. Relatively small research domains are receiving increasing attention, as evidenced by the negative correlation between the number of articles and D1 and D2 scores. The results show that, despite the limitations of the method, it is possible to (1) identify fast-growing areas of research regardless of the number of articles, and (2) predict publication activity in the short term with satisfactory accuracy for practice (the average prediction error for the year ahead is 6%, with a standard deviation of 7%). This paper presents results for more than 400 search queries related to classified research areas and the application of machine learning models to industries. The proposed method evaluates the dynamics of growth and the decline of scientific domains associated with certain key terms. It does not require access to large bibliometric archives and allows to relatively quickly obtain quantitative estimates of dynamic indicators. View Full-Text
Sustainable development of megacities requires a transition to the new management methods and technologies, based on the wide use of a large amount of heterogeneous data. Managing the urban economy needs to consider environmental restrictions, environmental monitoring tasks, engineering facilities, and transport. Operational control over the urban environment and the surrounding area can be produced using unmanned aerial vehicles (UAVs), and the collected data can be processed using a wide range of software and hardware technologies related to the field of artificial intelligence. However, along with any fairly new technology, intelligent unmanned technologies have both advantages and disadvantages. Strengths are mobility and efficiency, relative cheapness, the possibility of a high degree of automation, whereas weaknesses are short flight time, dependence on weather conditions, the certain outstanding tasks of data management and processing. This paper considers the possibilities of using intelligent unmanned technologies based on UAVs for solving the problems of monitoring the urban environment of the Kazakhstan megalopolises. Consideration is also being given to the scope for extending possibilities of applying these technologies to the field of environmental monitoring, monitoring of hazardous geological processes, technical constructions and vehicles. Furthermore, technological and economic issues, as well as necessary data processing technologies, are discussed. The economic effect of the use of IUVAT is estimated at $ 70-200 million, but it requires solving a set of data processing, control and technical problems.
4. P. Sedlacek, M Ospanova, M Yelis. Sensitivity analysis of MVL Systems by the Logic Derivatives of MVL Functions //CERes Journal.- 2021.- Vol.6.- Issue 2.- 2020. P.1-7 — http://ceres-journal.eu/download.php?file=2020_02_01.pdf5. Mukhamediev R. I. et al. Rapid bibliometric analysis in deep learning domain //2021 International Conference on Information and Digital Technologies (IDT). – IEEE, 2021. – С. 206-211. — https://ieeexplore.ieee.org/abstract/document/94975916. R Muhamedyev, K Yakunin, Y Kuchin, A Symagulov, S Murzakhmetov, M Ospanova, I Assanov, M Yelis. Intelligent unmanned aerial vehicle technologies // The 18th INTERNATIONAL CONFERENCE INFORMATION TECHNOLOGIES AND MANAGEMENT 2020 April 23-24, 2020, ISMA University of Applied Science, Riga, Latvia.- c. 21-22.7. Assanov I. Multi UAV simulator in Unity // The 19th INTERNATIONAL SCIENTIFIC CONFERENCE INFORMATION TECHNOLOGIES AND MANAGEMENT 2021 April 22-23, 2021, ISMA University of Applied Science, Riga, Latvia.- c. 46-47.8. A Bekbaganbetov, M Ospanova, M Yelis, J Rabcan, R Muhamedyev. Experiments to identify changes in synthesized images // The 19th INTERNATIONAL SCIENTIFIC CONFERENCE INFORMATION TECHNOLOGIES AND MANAGEMENT 2021 April 22-23, 2021, ISMA University of Applied Science, Riga.- c. 54-55.9. M Ospanova, M Yelis, A Bekbaganbetov, J Rabcan, R Muhamedyev Image generation for solving problems of precision farming // The 19th INTERNATIONAL SCIENTIFIC CONFERENCE INFORMATION TECHNOLOGIES AND MANAGEMENT 2021 April 22-23, 2021, ISMA University of Applied Science, Riga.- c. 64-65.10. R Muhamedyev, K Yakunin, Y Kuchin, A Symagulov, S Murzakhmetov, M Ospanova, I Assanov, M Yelis. Intelligent unmanned aerial vehicle technologies // The 18th INTERNATIONAL CONFERENCE INFORMATION TECHNOLOGIES AND MANAGEMENT 2020 April 23-24, 2020, ISMA University of Applied Science, Riga, Latvia.- c. 21-22