The goal of this thesis is to analyze the use of object detection in solving weed extirpation issues in agriculture. First, I will give an overview of different weed detection systems and analyze the advantages and disadvantages of the state-of-the-art object detection algorithms. In particular, I will focus on YOLO as a single-stage detector and Faster R-CNN as a two-stage detector. To this end, I have created a dataset of images submitted by farmers and labeled them with crops, weeds, weed roots, and rows of crops using the LabelImg tool. I run several experiments on this dataset and compare the two detectors in terms of evaluation metrics.