Computer vision methods and algorithms for automatic detection and classification of objects in decision support systems in agriculture
Alena YablokovaKrasnoyarsk Science and Technology City Hall of the Russian Union of Scientific and Engineering Public Associations, Krasnoyarsk, RussiaИ В КовалевKrasnoyarsk State Agrarian University, Krasnoyarsk, RussiaDmitry KovalevKrasnoyarsk Science and Technology City Hall of the Russian Union of Scientific and Engineering Public Associations, Krasnoyarsk, RussiaValeria PodoplelovaKrasnoyarsk State Agrarian University, Krasnoyarsk, 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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