Automatic Egg Counting with Computer Vision and IoT

In this paper an automated egg counting system for conveyor belts using YOLOv11 detection and ByteTrack tracking is proposed. Nano and small YOLOv11 models are compared, achieving approximately $90 \%$ accuracy with low inference time. Results demonstrate that nano models provide comparable performance to larger variants while maintaining computational efficiency for agricultural automation.