Development and adaptation of computer vision and machine learning methods for solving precision agriculture problems using unmanned aerial systems (AP14869972)
The project is aimed at solving monitoring problems to support precision agriculture technologies. During the project implementation, computer vision and machine learning methods will be developed to solve the problems of classifying and identifying images, including multispectral ones, obtained using UAVs. Tasks to be solved: classification of weeds, identification of stresses of useful plants. During the implementation of the project, the factors hindering the development of useful plants will be recreated, large sets of labeled images will be created and models will be trained, including deep neural networks, with the help of which the above tasks will be solved.
Objective of the project:
To develop and adapt computer vision and machine learning methods for solving precision agriculture problems by processing data and images obtained using unmanned aerial systems.
- Development of a multifunctional software and hardware system for the collection and preprocessing of images and data in order to solve the problems of precision agriculture;
- Formation of an experimental site for the reproduction of negative factors affecting the processes of precision agriculture;
- Formation of data sets for solving problems of precision agriculture using machine learning and UAVs;
- Development of methods for identifying and classifying negative factors in order to estimate their influence on the development of useful plants;
- Experimental evaluation of the developed methods and reporting.
The main objects of research and experiments are shown in the figure.
- Mukhamediev, R.; Amirgaliyev, Y.; Kuchin, Y.; Aubakirov, M.; Terekhov, A.; Merembayev, T.; Yelis, M.; Zaitceva, E.; Levashenko, V.; Popova, Y.; Symagulov, A.; Tabynbayeva, L. Operational Mapping of Salinization Areas in Agricultural Fields Using Machine Learning Models Based on Low-Altitude Multispectral Images. Drones 2023, 7, 357. https://doi.org/10.3390/drones7060357(Scopus: Q1, 75%, WoS: Q2, IF:4.8)
- Zaitseva, E., Levashenko, V., Mukhamediev, R., Brinzei, N., Kovalenko, A., & Symagulov, A. (2023). Review of Reliability Assessment Methods of Drone Swarm (Fleet) and a New Importance Evaluation Based Method of Drone Swarm Structure Analysis. Mathematics, 11(11), 2551. https://www.mdpi.com/2227-7390/11/11/2551
- Mukhamediev R. I. Yakunin, K., Aubakirov, M., Assanov, I., Kuchin, Y., Symagulov, A., Levashenko V., Zatceva E., Sokolov D., Amirgaliyev, Y. . Coverage path planning optimization of heterogeneous UAVs group for precision agriculture //IEEE Access. – 2023. – Т. 11. – №. 15. – С. 5789-5803, doi: 10.1109/ACCESS.2023.3235207, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10011226 (Scopus Quartile: Q1, 90%, JCR Category Quartile: Q2, WoS IF=3.476)
- Zaitseva, E., Levashenko, V., Brinzei, N., Kovalenko, A., Yelis, M., Gopejenko, V., & Mukhamediev, R. (2023). Reliability Assessment of UAV Fleets. In Emerging Networking in the Digital Transformation Age: Approaches, Protocols, Platforms, Best Practices, and Energy Efficiency(pp. 335-357). Cham: Springer Nature Switzerland. https://www.researchgate.net/profile/Elena-Zaitseva-2/publication/369409989_Reliability_Assessment_of_UAV_Fleets/links/641d5db6a1b72772e42293f8/Reliability-Assessment-of-UAV-Fleets.pdf
- Mukhamediev Ravil, Merembayev Timur, Symagulov Adilkhan, Kuchin Yan, Jan Rabcan. Determination of soil salinity using a UAV// The 21st INTERNATIONAL CONFERENCE INFORMATION TECHNOLOGIES AND MANAGEMENT 2023, April 20-21, 2023, ISMA University of Applied Sciences, Riga, Latvia
- Symagulov Adilkhan, Kuchin Yan, Jan Rabcan, Nadezhda Nikitina, Ravil Mukhamedyev, Laila Tabynbaeva. Unmanned aerial platform prototype with a multifunctional hardware and software system for acquiring and processing images and data for precision agriculture// The 21st INTERNATIONAL CONFERENCE INFORMATION TECHNOLOGIES AND MANAGEMENT 2023, April 20-21, 2023, ISMA University of Applied Sciences, Riga, Latvia
- Symagulov Adilkhan, Kuchin Yan, Jan Rabcan, Laila Tabynbaeva. Using UAVs and machine learning to generate plotted data sets for precision farming// The 21st INTERNATIONAL CONFERENCE INFORMATION TECHNOLOGIES AND MANAGEMENT 2023, April 20-21, 2023, ISMA University of Applied Sciences, Riga, Latvia