Computer vision methods and algorithms for automatic detection and classification of objects in decision support systems in agriculture
Alena YablokovaKrasnoyarsk State Agrarian University, Krasnoyarsk, RussiaИ В КовалевReshetnev Siberian State University of Science and Technology, Krasnoyarsk, RussiaDmitry KovalevNational Research University «Tashkent Institute of Irrigation and Agricultural Mechanization Engineers", Tashkent, UzbekistanValeria PodoplelovaSochi State University, Sochi, RussiaAziza KobilovaBukhara State University, Bukhara, Uzbekistan
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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.
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