| Issue |
E3S Web Conf.
Volume 548, 2024
X International Conference on Advanced Agritechnologies, Environmental Engineering and Sustainable Development (AGRITECH-X 2024)
|
|
|---|---|---|
| Article Number | 03023 | |
| Number of page(s) | 8 | |
| Section | Information Technologies, Automation Engineering and Digitization of Agriculture | |
| DOI | https://doi.org/10.1051/e3sconf/202454803023 | |
| Published online | 12 July 2024 | |
Computer vision methods and algorithms for automatic detection and classification of objects in decision support systems in agriculture
1 Krasnoyarsk Science and Technology City Hall of the Russian Union of Scientific and Engineering Public Associations, Krasnoyarsk, Russia
2 Krasnoyarsk State Agrarian University, Krasnoyarsk, Russia
3 Siberian Federal University, Krasnoyarsk, Russia
4 Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia
5 Navoi State University of Mining and Technology, Navoi, Uzbekistan
6 National Research University «Tashkent Institute of Irrigation and Agricultural Mechanization Engineers", Tashkent, Uzbekistan
7 Sochi State University, Sochi, Russia
8 Bukhara State University, Bukhara, Uzbekistan
* Corresponding author: alena.yabl@yandex.ru
The paper examines aspects of developing and formalizing the task of applying computer vision methods and algorithms using OpenCV (implemented in Python version 3.13 notation) for automatic detection and classification of objects in decision support systems. A software implementation of a modular example is provided, enabling automatic detection and classification for the detection of plant diseases based on their external characteristics in decision support systems in agriculture. This approach will facilitate prompt response to plant diseases and the implementation of necessary measures for their treatment.
© The Authors, published by EDP Sciences, 2024
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