In precision poultry farming, detecting multiple objects is challenging. While Convolutional Neural Networks (CNNs) excel in single-object detection, addressing the complexity of multi-object detection, requires advanced approaches. To overcome this challenge, we implement advanced algorithms like YOLOv8, SSD, and Faster RCNN. The primary goal is to analyze their performance, focusing on accuracy, speed, and adaptability. We aim to balance computational efficiency with optimal resource utilization, considering hardware constraints. Integrating these models into precision farming systems and adapting to environmental variations are key challenges. This project specifically aims to validate YOLOv8's effectiveness, and yields an accuracy around 98% in poultry farming scenarios. This research contributes insights to advance precision poultry farming practices.