Bees are crucial in pollination, significantly impacting ecosystem health and agricultural productivity. The decline in population poses a threat to biodiversity and food security. Understanding bee behavior is essential for assessing their health and the impacts of environmental stressors, such as habitat loss and pesticide exposure. This study aims to use the YOLOv9 algorithm for bee detection in videos, building on previous research using YOLO algorithms for insect studies. The proposed method will be tested on videos of bees moving in a controlled environment, with preprocessing techniques to address lighting reflections. The goal is to improve upon existing models and, in future work, develop a desktop application for automated bee behavior analysis, providing valuable insights for conservation and agricultural practices.