This study explores the potential of improving weed identification accuracy and reducing herbicide usage through the use of computer vision analysis, specifically incorporating imaging datasets that include both RGB and near-infrared (NIR) channels. The thesis investigates the application of deep learning models, namely YOLO and U-Net, to improve the detection of weeds and their semantic segmentation in precision agricultural contexts. It utilizes two meticulously annotated datasets: ACRE for object detection and Sunflower for semantic segmentation. Additionally, the study considers the effect of varying NDVI thresholds on segmentation performance, finding that fine-tuning these thresholds can be key to enhancing outcomes. The customized U-Net model deployed for segmentation offers promising results, outpacing several leading models in effectiveness, particularly with integrating NIR data.