This paper presents a comparative analysis of YOLOv11 and YOLOv12 for automated weed detection in precision agriculture. The primary objective is to assess both models’ detection accuracy, generalization ability, and reliability using a custom-annotated dataset of sesame crop and weed images. YOLOv11, known for its faster inference speed, demonstrates higher mAP@0.5 in straightforward detection scenarios. However, YOLOv12 outperforms in challenging conditions due to its advanced architectural enhancements, including attention mechanisms and improved feature pyramids. This study highlights the trade-off between computational efficiency and robust detection, offering insights into choosing the optimal object detection model for real-time agricultural applications.