Technological advances in computer vision have moved from detecting and recognizing objects in an image, to understanding the relationships between objects and generating a textual description based on the image content, using an image dataset and scene graphs. Scene graphs are a structured representation of a scene that can express the objects, attributes and relationships between objects in the scene, in order to textually describe the image content. In this research work, we present the creation of a dataset of bee images called “Bees” and the generation of scene graphs from this dataset, in order to recognize, classify and analyze the organizational behavior of bees at the entrance of the beehive, for the sake of the preservation and care of this endangered species. We created an image dataset of 100 images and their constituent parts necessary for the generation of the scene graphs. The method used is described and evaluated with a data set of 500 images. In the generation of scene graphs, the experiments performed show on average 69% of accuracy in the recognition of objects and their bounding boxes, and on average 68% of accuracy in scene recognition of the bees at the entrance of the beehive.