Animal detection in farm environments is an important task for precision livestock farming and animal welfare monitoring. The Single Shot Detector (SSD) algorithm has shown promising results in object detection tasks, including animal detection. In this paper, we propose a method for animal detection in farm environments using the SSD algorithm. We use a dataset of aerial imagery and ground-level images collected from farms to train the SSD model. The proposed method achieves high accuracy in detecting various types of animals, including cows, pigs, and chickens. The system is designed to work in real-world farm environments and can be deployed on unmanned aerial vehicles (UAVs) for real-time animal detection. The proposed method can be used for precision livestock farming and animal welfare monitoring, which can help farmers optimize their operations and improve animal welfare.