BR24992908 — Support system for agricultural crop production optimization via remote monitoring and artificial intelligence methods (Agroscope)

Goal of the work: The emergence of new professions that lie at the intersection of agricultural sciences, data science and robotics

Relevance of the work: The results of the program will contribute to the development of the agricultural UAV market with an estimated volume of several tens of millions of dollars. Improving the educational level of specialists by introducing the results of the program into the educational process.

Scientific supervisor: Candidate of technical sciences, Professor, Ravil Mukhamediev

Results obtained:  In 2025, within the framework of the project, hexacopter and tilt-rotor UAV platforms were developed and tested, along with onboard payload systems for agricultural field monitoring. A prototype of a ground-based hardware and software complex was created and deployed, incorporating high-performance computing resources for data storage, processing, and analysis. Software tools for image segmentation and classification based on satellite and UAV data were developed, and annotated datasets for machine learning models were prepared. Methods and prototypes for flight path planning were implemented using optimization and genetic algorithms. Models and software solutions were developed for crop growth stage identification, yield prediction, and crop stress assessment. In addition, a cartographic system for visualization of monitoring results was created, and all planned tasks were completed in full.

List of publications with links to them

1. Kadyrzhan A., Matrassulova D., Vitulyova Y., Suleimenov I. Discrete Cartesian coordinate transformations: using algebraic extension methods // Applied Sciences. – 2025. – Т. 15, № 3. – С. 1464. – DOI: https://doi.org/10.3390/app15031464.
2. Mukhamediev R. I., Smurygin V., Symagulov A., Kuchin Y., Popova Y., Abdoldina F., Tabynbayeva L., Gopejenko V., Oxenenko A. Fast detection of plants in soybean fields using UAVs, YOLOv8x framework, and image segmentation // Drones. – 2025. – Т. 9. – С. 547. – DOI: https://doi.org/10.3390/drones9080547.
3. Shaltykova D., Vitulyova Y., Kadyrzhan K., Suleimenov I. Application of partial discrete logarithms for discrete logarithm computation // Computers. – 2025. – Т. 14. – С. 343. – DOI: https://doi.org/10.3390/computers14090343.
4. Sysoyev A., Nauruzov K., Karati A., Abramkina O., Vitulyova Y., Yeskendirova D., Popova Y., Abdoldina F. Lightweight group signature scheme based on PUF for UAV communication security // Drones. – 2025. – Т. 9. – С. 693. – DOI: https://doi.org/10.3390/drones9100693.
5. Yakunin K., Kuchin Y., Muhamedijeva E., Symagulov A., Mukhamediev R. I. An algorithm for planning coverage of an area with obstacles with a heterogeneous group of drones using a genetic algorithm and parameterized polygon decomposition // Drones. – 2025. – Т. 9. – С. 658. – DOI: https://doi.org/10.3390/drones9090658.
6. Якунин К., Құсайын Д., Мухамедиев Р. И., Юничева Н., Кульдеев Н. Сопряжение программ планирования маршрутов полета и систем управления беспилотных летательных аппаратов // Известия НАН РК. Физико-математическая серия. – 2025. – № 2 (354). – С. 317–335. – DOI: https://doi.org/10.32014/2025.2518-1726.359.
7. Якунин К. О., Сымагулов А., Мухамедиев Р. И., Юничева Н. Р. Планирование маршрутов облета сельскохозяйственных полей для гетерогенной группы беспилотных летательных аппаратов // Вестник АГА. – 2025. – № 2 (37). – С. 79–89. – DOI: https://doi.org/10.53364/24138614_2025_37_2_7.
8. Savin I. Yu., Terekhov A. G., Mukhamediev R. I. Soil line concept and its use for soil mapping and monitoring (overview) // Бюллетень Почвенного института им. В. В. Докучаева. – 2025. – № 122. – С. 174–193. – DOI: https://doi.org/10.19047/0136-1694-2025-122-174-193.
9. Savin I. Yu., Terekhov A. G., Mukhamediev R. I. Use of ICE index for satellite monitoring of irrigation massifs and land conditions // Siberian Journal of Life Sciences and Agriculture. – 2025. – Т. 17, № 2. – С. 154–175. – DOI: https://doi.org/10.12731/2658-6649-2025-17-2-1128.
10. Смурыгин В. В., Еримбетова А. С., Кучин Я. И., Сымагулов А. Интеллектуальная система картирования и распознавания факторов на основе технологий дистанционного зондирования и компьютерного зрения // Вестник Казахстанско-Британского технического университета. – 2025. – Т. 22, № 3. – С. 161–175. – DOI: https://doi.org/10.55452/1998-6688-2025-22-3-161-175.