Resumen
In recent years, traditional farming methods have been increasingly replaced by more modern, intelligent farming techniques. This shift towards information and intelligence in farming is becoming a trend. When they are bitten by dinoflagellates, cows display stress behaviors, including tail wagging, head tossing, leg kicking, ear flapping, and skin fluttering. The study of cow protective behavior can indirectly reveal the health status of cows and their living patterns under different environmental conditions, allowing for the evaluation of the breeding environment and animal welfare status. In this study, we generated key point feature marker information using the DeepLabCut target detection algorithm and constructed the spatial relationship of cow feature marker points to detect the cow?s protective behavior based on the change in key elements of the cow?s head swinging and walking performance. The algorithm can detect the protective behavior of cows, with the detection accuracy reaching the level of manual detection. The next step in the research focuses on analyzing the differences in protective behaviors of cows in different environments, which can help in cow breed selection. It is an important guide for diagnosing the health status of cows and improving milk production in a practical setting.