ÓÄÊ 631.171
DOI 10.33861/2071-8020-2025-5-36-38
Original Empirical Research
Fomina A. S., Vasiliev P. V., Kochetkova N. A., Zelenkov A. P., Zelenkova G. A.
Abstract. Automation of behavioral analysis opens up new horizons in the management of breeding farms. Accurate identification of stress-related behavioral patterns makes it possible to quickly adjust the conditions of detention, minimizing the negative impact on egg productivity and the general condition of the bird. For example, the detection of increased crowding in a certain area may signal the need to redistribute feeders or create additional shelters. Based on behavioral data, the system signals the need to optimize the conditions of keeping in order to achieve maximum productivity and well-being of the bird. This approach will reduce energy and resource costs, as well as reduce dependence on manual control. The implementation of the developed system is not limited to breeding farms. It can be adapted to monitor the behavior of wild duck populations in natural conditions. The development of computer vision and machine learning technologies opens up great opportunities for automating the analysis of animal behavior. The creation of intelligent monitoring systems will not only improve the efficiency of wildlife management, but also make a significant contribution to wildlife conservation and sustainable resource management. Further research in this area will contribute to the development of more accurate, reliable and cost-effective monitoring systems capable of solving a wide range of environmental problems.
Key words: mallard duck, wild bird, wild breeding, technological stress, computer vision, artificial intelligence, behavioral activity, productivity, system, technology.
Author affiliation:
Fomina Anna S., Ph. D. in Biology, Docent, Docent of the Department of Biology and General Pathology of the Don State Technical University; 1, Gagarina sq., Rostov-on-Don, 344003; phone: 8-908-1742627; e-mail: a_bogun@mail.ru.
Vasiliev Pavel V., Ph. D. of Technics, Docent of the Department of Information Technology of the Don State Technical University; 1, Gagarina sq., Rostov-on-Don, 344003; phone: 8-918-5036280; e-mail: lyftzeigen@mail.ru.
Kochetkova Nataliya A., Senior Lecturer of the Department of Biology and General Pathology of the Don State Technical University; 1, Gagarina sq., Rostov-on-Don, 344003; phone: 8-988-5569758; e-mail: les.nata13@bk.ru.
Zelenkova Galina A., D.Sc. in Agriculture, Professor of the Department of Biology and General Pathology of the Don State Technical University; 1, Gagarina sq., Rostov-on-Don, 344003; phone: 8-961-3096244; e-mail: galinazelenkova2025@gmail.com.
Responsible for correspondence with the editorial board: Zelenkov Alexey P., D. Sc. in Agriculture, Professor of the Department of Biology and General Pathology of the Don State Technical University; 1, Gagarina sq., Rostov-on-Don, 344003; phone: 8-951-8176966; e-mail: zelenkovalex@rambler.ru.
Authors’ Contribution: the manuscript was written with the input of all authors. All authors approved the final version of the manuscript.
Conflict of Interest Statement: the authors declare no conflict of interest.
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