AP14869972 – Development and adaptation of computer vision and machine learning methods for solving precision agriculture problems using unmanned aerial systems

Term of realization: 2022-2024 yy.

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.

Project tasks:

1. 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;
2. Formation of an experimental site for the reproduction of negative factors affecting the processes of precision agriculture;
3. Formation of data sets for solving problems of precision agriculture using machine learning and UAVs;
4. Development of methods for identifying and classifying negative factors in order to estimate their influence on the development of useful plants;
5. Experimental evaluation of the developed methods and reporting.

Results:

3. A prototype of a multifunctional software and hardware system for collecting and preprocessing images and data has been developed in order to solve the problems of precision farming. The prototype includes an unmanned aerial platform with suspension elements, including an accurate positioning system with a load capacity of up to 1 kg and software for processing images obtained using this platform. The software includes, in varying degrees of readiness, data reading functions (uploading images of different spectral ranges), image preprocessing functions and calculation of spectral indices, machine learning model training functions, map generation functions.
4. Four experimental plots with an area of 135 m2 each have been created, in total the plots amount to 540 m2. Sugar beet is planted on the plots. Negative factors:
   1. The first section implements a shortage of fertilizers in the soil;
   2. The second section implements the lack of treatment from weeds;
   3. The third section realizes the lack of moisture (irrigation);
   4. The fourth section contains all three previous negative factors.
5. Marked up 300+ photos of the soybean field at the first stage of growth. In each photo, weeds and a useful crop (soy) are individually highlighted. Weeds of 9-10 species (Amaranthus retroflexus, Convolvulus arvensis, Setaria glauca, Xanthium strumarium, Cirsium arvense, Echinochloa crusgalli, Hibiscus trionum, Abutilon theophrasti, Chenopodium album, Apera spica-venti), one useful culture (Glycine max).
6. A prototype of a method for identifying weeds of several species has been developed. Work is being carried out to improve the accuracy of the developed methods and their experimental approbation.

Publications:

1. 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)
2. 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
3. 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)
4. 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. 
5. 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
6. 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
7. 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