This study aimed to develop a real-time method for detecting and selecting birds in video images using artificial intelligence. The objectives included creating a reliable method for isolating bird signals against varying terrain backgrounds using neural networks, estimating bird numbers in frames through AI-driven threshold techniques, and proposing a solution for managing pest bird populations by analyzing video data to control electronic deterrents. Throughout the research, we identified the bird species present on the premises of brewery across different seasons, compiled an annotated species list, and established a database of granary birds. Leveraging the YOLO architecture based on artificial intelligence, we developed a program for bird detection in low-resolution, low-quality images. The system underwent laboratory and field testing to validate its effectiveness.