The article explores the use of computer vision technologies to automate the process of observing animals in open spaces, with the aim of counting and identifying species. It discusses advanced methods of animal detection and recognition through the use of highly accurate neural networks. A significant challenge addressed in the study is the issue of duplicate animal counts in image data. To overcome this, two approaches are proposed: the analysis of video data sequences and the individual recognition of animals. The advantages and limitations of each method are analyzed in detail, alongside the potential benefits of combining both techniques to enhance the system's accuracy. The study also describes the process of training a neural network using a specialized dataset. Particular attention is given to the steps involved in data preparation, augmentation, and the application of neural networks like YOLO for efficient detection and classification. Testing results highlight the system's success in detecting animals, even under challenging conditions. Moreover, the article emphasizes the practical applications and potential of these technologies in monitoring animal populations and improving livestock management. It is noted that these advancements could contribute significantly to the development of similar systems in agriculture. The integration of such technologies is presented as a promising solution for tracking animal movement, assessing their health, and minimizing livestock losses across vast grazing areas.

Keywords: algorithm, computer vision, monitoring, pasture-based, livestock farming