Optimizing corn production through digital and smart technologies

V.M. Shuganov, A.Kh. Shogenov, Z.Yu. Kantiev, R.V. Bizhoev

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Abstract: In the face of ongoing global challenges such as population growth, climate change, and soil degradation, the need to increase grain yields through digital transformation is becoming an urgent issue. Smart farming, through the widespread use of digital and intelligent technologies, robotics, and unmanned aerial vehicles, is crucial for increasing grain production and efficient resource management. One of the primary applications of unmanned aerial vehicles is multispectral imaging for effective agricultural monitoring, providing crop producers with detailed information on crop condition, which is particularly important for the prompt and timely implementation of management tasks related to grain crop breeding and seed production.
Aim. The paper aims to explore the monitoring and differentiated crop protection against common smut using digital and smart technologies.
Materials and methods. The study involves systematic monitoring for corn smut symptoms and subsequent spraying using various modern technologies, including traditional and unmanned aerial vehicles (UAVs). The research was conducted at an experimental site in the rural settlement of Opytnoye in the Tersky Municipal District, Kabardino-Balkarian Republic, during corn crops in 2022–2024. Operational monitoring of corn plots was conducted using a DJI Mavic 3M UAV, while the DJI AGRAS MG-1 and DJI Agras T10 were used for crop spraying in various years.
Results. This article presents the results of studies using different technical parameters of UAVs for multispectral monitoring and differentiated application of plant protection products to corn crops. Using a DJI Mavic 3M unmanned aerial vehicle (UAV) at an altitude of 150 m with automatic flight speed control from 5 to 9 m/s enables multispectral imaging, ensuring high-performance and high-quality phenotyping of grain crops. Variable-rate fungicide application using a DJI Agras T10 UAV at a working solution concentration of 75% of the recommended rate resulted in a 10.7–22.6% increase in yield in the experimental plots compared to the control plot.
Conclusion. The optimal fungicide application schedule in the experimental plots not only ensured uniform plant coverage and increased effectiveness in controlling common smut, but also reduced the cost of crop protection products, reduced chemical loads, and preserved the soil and environment.

Keywords: digital and smart technologies, corn crops, bladder smut, drones, multispectral imaging, monitoring, spraying

For citation. Shuganov V.M., Shogenov A.Kh., Kantiev Z.Yu., Bizhoev R.V. Optimizing corn production through digital and smart technologies. News of the Kabardino-Balkarian Scientific Center of RAS. 2025. Vol. 27. No. 5. Pp. 200–210. DOI: 10.35330/1991-6639-2025-27-5-200-210

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Information about the authors

Vladislav M. Shuganov, Doctor of Agricultural Sciences, Head of the Scientific and Innovation Center “Intelligent Systems and Environments of Food Production and Consumption”, Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
2 Balkarov street, Nalchik, 360010, Russia;
vmshuganov@mail.ru, ORCID: https://orcid.org/0000-0002-5189-998X, SPIN-code: 6767-7554
Anzor Kh. Shogenov, Candidate of Agricultural Sciences, Researcher, Laboratory of Intelligent Distribution Agricultural Systems, Scientific and Innovation Center “Intelligent Systems and Environments of Food Production and Consumption”, Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
2 Balkarov street, Nalchik, 360010, Russia;
a.vonegosh@yandex.ru, ORCID: https://orcid.org/0000-0002-1184-5397, SPIN-code: 5996-3532
Zaurbek Yu. Kantiev, Junior Researcher, Agricultural Robotics Laboratory, Scientific and Innovation Center “Intelligent Systems and Environments of Food Production and Consumption”, Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
2 Balkarov street, Nalchik, 360010, Russia;
kantvako@gmail.com, ORCID: https://orcid.org/0000-0003-4997-1177
Ruslan V. Bizhoev, Researcher, Agricultural Robotics Laboratory, Scientific and Innovation Center “Intelligent Systems and Environments of Food Production and Consumption”, Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
2 Balkarov street, Nalchik, 360010, Russia;
bizhoeva49@mail.ru, ORCID: https://orcid.org/0000-0001-5408-3006, SPIN-code: 4821-0152