Control system and aerial imaging equipment for unmanned aircraft used in agricultural land monitoring

Unmanned aircraft systems are becoming one of the key technologies in the digital transformation of the agro-industrial sector, enabling timely monitoring of agricultural land and the acquisition of up-to-date information on its condition. (Research purpose) The study aimed to conduct a retrospective analysis to identify the main trends in the development of UAV flight control systems and aerial imaging equipment for agricultural land monitoring. (Materials and methods) A retrospective analysis was carried out based on a systematic literature review. Original works by Russian and foreign authors were examined, along with monographs, conference proceedings, museum exhibits, photographic documents, and open-source software code. (Results and discussion) Six stages were identified in the development of UAV flight control systems and aerial imaging equipment used for agricultural land monitoring. The proposed periodization is based on changes in the types of image data collected, the cameras employed, and the aircraft on which they were installed. For each stage, the main trends in the development of UAV flight control systems and aerial imaging equipment were determined, including UAV type, flight range and endurance, flight control system, imaging equipment, type and number of images obtained, and the aircraft used for data collection. Based on the analysis, further directions for the development of unmanned aircraft systems were identified. (Conclusions) The development of UAV flight control systems and aerial imaging equipment proceeded in parallel until cameras were integrated directly on board aircraft. The study showed that camera miniaturization and the improvement of flight controllers have played a key role in this development, ensuring high-accuracy aerial imaging data and enabling timely monitoring of agricultural land. Further development of onboard intelligent systems and aerial imaging equipment is expected to lead to the full automation of data collection and processing, thereby enabling high-precision real-time monitoring.

1. Gaysin R.S. The limit of technological evolution in agriculture and the possibility of overcoming it. Problems of Modern Economics. 2014. N4(52). 41-45 (In Russian). EDN: TJHWFH.

2. Gusev E.M. Evolution of agricultural technologies: from «gray» to «green». Arid Ecosystems. 2020. Vol. 26. N1(82). 3-12 (In Russian). EDN: CZCAHB.

3. Korotchenya V.M. History of technological development of agriculture (crop production). Economics of Agriculture of Russia. 2019. N7. 28-33 (In Russian). DOI: 10.32651/197-28.

4. Eremin S.G. Digital transformation of agriculture: opportunities and challenges of implementing big data technologies. Agrarian Science. 2025. N2. 36-38 (In Russian). EDN: NILBNC.

5. Vinnichek L.B., Omarov M.M., Omarova N.Yu. Problems and promising areas of development of agriculture in Russia until 2030. Economics of Agriculture of Russia. 2025. N6. 2-12 (In Russian). DOI: 10.32651/256-2.

6. Lobachevsky Ya.P., Lachuga Yu.F., Izmailov A.Yu., Shogenov Yu.Kh. Scientific and technical achievements of agroengineering science in the conditions of digitalization of agriculture. Russian Agricultural Sciences. 2025. N3. 45-53 (In Russian). DOI: 10.31857/S2500262725030081.

7. Gostev A.V. Development of agriculture digitalization on the example development of modern agricultural technologies using digital systems. Achievements of Science and Technology in Agro-Industrial Complex. 2025. Vol. 39. N1. 4-9 (In Russian). DOI: 10.53859/02352451_2025_39_1_4.

8. Alt V.V., Tsench Yu.S., Savchenko O.F., Soloshenko A.A. Methodological foundations for the integration of unmanned aerial vehicles into agricultural machinery systems. Russian Agricultural Sciences. 2025. N4. 59-67 (In Russian). DOI: 10.7868/S3034582025040114.

9. Lachuga Yu.F., Lobachevsky Ya.P., Alferov A.A. Agricultural science in the development of the agro-industrial complex of the Russian Federation. Herald of the Russian Academy of Sciences. 2025. N6. 3-8 (In Russian). DOI: 10.7868/S3034520025060019.

10. Kurbanov R.K., Tsench Yu.S., Zakharova N.I. Major trends in the development of aerial photography technology for agricultural lands. Agricultural Machinery and Technologies. 2025. Vol. 19. N1. 86-95 (In Russian). DOI: 10.22314/2073-7599-2025-19-1-86-95.

11. Krasnopevtsev B.V. Main events in the history of photogrammetry and aerial surveying before 1918. Geodesy and Cartography. 1998. N8. 55-59 (In Russian).

12. Tsench Yu.S., Kurbanov R.K. History of unmanned aircraft flight controller development. Agricultural Machinery and Technologies. 2023. 17(3). 4-15 (In Russian). DOI: 10.22314/2073-7599-2023-17-3-4-15.

13. Monmonier M. Aerial photography at the Agricultural Adjustment Administration: acreage controls, conservation benefits, and overhead surveillance in the 1930s. Photogrammetric Engineering & Remote Sensing. 2002. N68(12). 1257-1261.

14. Antipov I.T. Development of photogrammetry in Russia. Geo-Siberia. 2010. NS. 97-132 (In Russian). EDN: PFOGOP.

15. Komissarov A.V., Golovina L.A. History of the development of the Department of Photogrammetry and Remote Sensing. Vestnik Siberian State University of Geosystems and Technologies. 2023. Vol. 28. N6. 173-179 (In Russian). DOI: 10.33764/2411-1759-2023-28-6-173-179.

16. Goldman L.M. Application of color aerial photography for terrain studies (interpretation of color aerial photographs). Proceedings of the Central Research Institute of Geodesy, Aerial Survey and Cartography. 1960. 137. 57-63 (In Russian).

17. Mulla D.J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering. 2013. N114(4). 358-371.

18. Reyes-Rosas A., Lara-Viveros F.M., Chávez-Cerón L. et al. Estimation of water potential in corn plants using machine learning techniques with UAV imagery and evaluating the effect of flying height. Engineering Proceedings. 2023. 56(1). 157. DOI: 10.3390/ASEC2023-15882.

19. Nesar S.B., Nugent P.W., Zidack N.K. et al. Unmanned aerial vehicle-based hyperspectral imaging for potato virus Y detection: machine learning insights. Remote Sensing. 2025. 17(10). 1735. DOI: 10.3390/rs17101735.

20. Sarkar S., Zhou J., Scaboo A. et al. Assessment of soybean lodging using UAV imagery and machine learning. Plants. 2023. 12(16). 2893. DOI: 10.3390/plants12162893.

21. Tueros M., Galindo M., Alvarez J. et al. Varietal Identification and yield estimation in potatoes using UAV RGB imagery in the southern highlands of Peru. AgriEngineering. 2026. 8(2). 65. DOI: 10.3390/agriengineering8020065.