AP08856412 — Development of intelligent data processing and flight planning models for solving the problems of precision farming using UAVs

Objective of the project: Development of data processing and flight planning models for technically heterogeneous UAVs for solving precision farming problems based on artificial intelligence methods.

Relevance: The use of UAVs for solving a wide range of monitoring and control tasks in the economic sectors of Kazakhstan is limited not only by the individual technical features of this mobile platform, but also by the insufficient development of practically applicable intelligent methods, algorithms and systems for traffic control and analysis of data coming from the UAV. The project is aimed at developing practically applicable methods that provide solving the problems of flight control (including a group of devices), identification and classification of objects of observation, using modern machine learning methods to solve precision farming problems. Expected results are applicable in other industries to solve monitoring problems.

Scientific adviser: Doctor of technical sciences, Professor, Ravil Muhamedyev

Results obtained: Within the project, methods for UAV control and data processing for precision agriculture were developed. More than 1,000 labeled datasets were created for training machine learning and deep learning models. Data preprocessing techniques, including anomaly and missing data handling, were designed. Algorithms for object classification and identification were developed and validated. Software solutions and flight planning algorithms adaptable to various conditions were implemented. Simulation environments and prototypes were tested under real conditions, confirming their effectiveness. The results were integrated into research and education, supported by publications and intellectual property outputs.

List of publications with links to them

1. Mukhamedyev R., Kuchin Y., Yakunin K., Symagulov A., Ospanova M., Assanov I., Yelis M. Intelligent unmanned aerial vehicle technology in urban environments // Communications in Computer and Information Science: International Conference on Digital Transformation and Global Society. – Cham: Springer, 2020. – 16 p. – URL: https://www.dropbox.com/s/5kiex8t1cb6krkn/paper_84%2B.pdf?dl=0
2. Mukhamediev R.I., Symagulov A., Kuchin Y., Yakunin K., Yelis M. From Classical Machine Learning to Deep Neural Networks: A Simplified Scientometric Review // Applied Sciences. – 2021. – Vol. 11, No. 12. – P. 5541. – DOI: https://doi.org/10.3390/app11125541
3. Mukhamediev R.I. et al. Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country // Applied Sciences. – 2021. – Vol. 11, No. 21. – P. 10171. – DOI: https://doi.org/10.3390/app112110171
4. Mukhamediev R.I. et al. Rapid bibliometric analysis in deep learning domain // Proceedings of the 2021 International Conference on Information and Digital Technologies (IDT). – IEEE, 2021. – P. 206–211. – URL: https://ieeexplore.ieee.org/abstract/document/9497591
5. Assanov I. Multi UAV simulator in Unity // The 19th International Scientific Conference “Information Technologies and Management”. – Riga: ISMA University, 2021. – P. 46–47. – URL: https://www.ismaitm.lv/images/Files/Theses/2021/01_NC/18_ITM2021_Assanov.pdf
6. Bekbaganbetov A., Ospanova M., Yelis M., Rabcan J., Muhamedyev R. Experiments to identify changes in synthesized images // The 19th International Scientific Conference “Information Technologies and Management”. – Riga: ISMA University, 2021. – P. 54–55. – URL: https://www.ismaitm.lv/images/Files/Theses/2021/01_NC/22_ITM2021_Bekbaganbetov_Ospanova_Yelisl_Rabcan_Muhamedyev.pdf
7. Ospanova M., Yelis M., Bekbaganbetov A., Rabcan J., Muhamedyev R. Image generation for solving problems of precision farming // The 19th International Scientific Conference “Information Technologies and Management”. – Riga: ISMA University, 2021. – P. 64–65. – URL: https://www.ismaitm.lv/images/Files/Theses/2021/01_NC/26_ITM2021_Ospanova_Yelis_Bekbaganbetov_Rabcan_Muhamedyev.pdf
8. Zaitseva E., Levashenko V., Brinzei N., Kovalenko A., Yelis M., Gopejenko V., Mukhamediev R. Reliability assessment of UAV fleets // Emerging Networking. – Springer, 2022. – URL: https://www.dropbox.com/s/ez36mxuj7blzk8t/reliability_assessment_UAV_springer_last_2.1.docm?dl=0
9. Symagulov A., Kuchin Y., Rabcan J., Kulakova Ye., Assanov I., Mukhamediev R. Pretrained Deep Neural Network Models for Image Change Detection // The 20th International Scientific Conference “Information Technologies and Management”. – Riga: ISMA University, 2022. – P. 20–21. – URL: https://www.dropbox.com/s/4xuh5ytjvm5x78u/08_ITM2022_Symagulov_Pretrained%20Deep%20Neural%20Network%20Models.pdf?dl=0
10. Symagulov A., Assanov I., Kuchin Y., Rabcan J., Kulakova Ye., Abdygalym B. Video pre-processing for computer vision tasks using UAVs // The 20th International Scientific Conference “Information Technologies and Management”. – Riga: ISMA University, 2022. – P. 25–26. – URL: https://www.dropbox.com/s/isivvdkd7lo7fp5/10_ITM2022_Symagulov_Video%20pre-processing%20for%20computer%20vision.pdf?dl=0
11. Мухамедиев Р.И., Амиргалиев Е.Н. Введение в машинное обучение: учебник. – Алматы, 2022. – 288 с. – ISBN 978-601-08-1177-5. – URL: https://geoml.info/%d0%ba%d0%bd%d0%b8%d0%b3%d0%b0/
12. Mukhamediev R. et al. Coverage path planning optimization of heterogeneous UAVs group for precision agriculture // IEEE Access. – 2022. – URL: https://www.dropbox.com/s/97sw747cjz4qg6f/IEEE_Access_Flight%20planning_format_v.1.63.docx?dl=0