This study explores the application of YOLOv8 in agricultural weed detection and compares the performance of other versions of YOLO, as well as the YOLOv8 model incorporating brain-inspired attention mechanisms, in terms of weed detection. The comparison shows that the YOLOv8 model enhances detection accuracy and speed through its advanced network architecture and image processing techniques. The incorporation of attention mechanisms further improves its detection capabilities by focusing on key features within images, optimizing the model's ability to discern subtle differences between weeds and crops. This improvement further increases its efficiency in complex agricultural environments. Experimental validation demonstrates that YOLOv8 significantly surpasses previous models in terms of precision and reliability, supporting its integration into precision agriculture. This research emphasizes the importance of adopting improved intelligent technologies like YOLOv8 to enhance the efficiency and sustainability of agriculture.