This work detects predators in extensive breeding farms, especially those dedicated to free-range chicken production. In this environment, the main threats are birds of prey and poachers. The proposal focuses on the first case to study how using deep learning models can help detect and label images. Specifically, we evaluate the performance of several object detection models on a dataset focused on birds of prey in outdoor farm settings. The study compares architectures from two families: Faster R-CNN and YOLO. Results show notable differences in performance between model families. Faster R-CNN shows a better performance than YOLO. This considerable difference marks the importance of selecting the model to be used, especially in cases such as this one, where the objects to be detected and classified are small in size.