Increasing cases of herbicide-resistant weeds and the labor-intensive nature of traditional weed control methods necessitate more efficient and accurate solutions in agriculture. This thesis addresses these challenges by evaluating YOLO series algorithms (YOLOv5 to YOLOv10) to determine the most effective model for detecting glyphosate-resistant Palmer amaranth, identifying YOLOv9 as the best performer with the highest mean average precision (mAP). We then compared YOLOv8, YOLOv9, YOLOv10, and Faster R-CNN models for multi-species weed detection and found YOLOv9 to be the most robust model. Finally, the study investigated the use of YOLOv9 algorithm for evaluating the effectiveness of various PRE and POST herbicides for weed control in 2,4-D/glufosinate/glyphosate-resistant (Enlist) soybeans. automates weed control ratings in Enlist soybean fields by correlating weed counts from drone imagery with visual control ratings. Altogether, the findings of this research demonstrated the potential for significantly improving weed management practices through species-specific weed identification using YOLO based object detection models highlighting the transformative impact of advanced machine learning algorithms on developing sustainable and economic weed management practices.