Operator support in flight control of a quadcopter in crop production tasks

Автор: Shushkov R.A., Rapakov G.G.

Журнал: АгроЗооТехника @azt-journal

Рубрика: Общее земледелие и растениеводство

Статья в выпуске: 4 т.8, 2025 года.

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Increasing the efficiency of agricultural production requires the introduction of digital technologies in modern crop production. One of the key tasks of the agro-industrial complex is the operational monitoring of fields and agricultural machinery. In this context, autonomous unmanned air vehicles with the function of tracking the operator are of particular interest, as they help automate the accompaniment of an agronomist when bypassing land; monitor the work of field equipment in real time; optimize routes across fields. The aim of the research is to develop an unmanned air vehicle operator tracking system that allows solving scientific and technical problems of stable tracking with limited computing resources, offering the use of available platforms to solve the problems of automatically following the operator; accounting for typical speeds of movement of an agronomist (5–7 km/h); reducing operator qualification requirements; management in conditions where when the support object is busy with their tasks; programming via the Python SDK; the use of neural network algorithms and computer vision methods, which determines the scientific novelty of the research. As a result, a reliable UAV operator tracking system has been developed based on a neural network approach, taking into account hardware limitations and optimizing object tracking parameters in real conditions. We received a certificate of state registration of the computer program “Operator support in the task of flight control of a quadcopter” RU 2025615266. The implementation of the operator's support system during project scaling determines the areas of application of the results obtained in the agro-industrial complex of the region and will reduce the labor costs of the agronomist for crop monitoring, increase the speed of field inspection, reduce crop losses due to early detection of problems, and improve the accuracy of agrochemical operations. The results of the work can be used in the educational process of educational organizations. Further prospects for research work are related to the integration of technology into agrotechnological processes of crop production, taking into account the specifics of agricultural partner enterprises in the region.

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Artificial intelligence, digital transformation, agro-industrial complex, unmanned air vehicles, computer vision

Короткий адрес: https://sciup.org/147252065

IDR: 147252065   |   УДК: 633/635:004.8   |   DOI: 10.15838/alt.2025.8.4.4