Abstract
Agriculture is one of the most ancient and most important professions forming the base of any society. The development of any country depends on the agricultural produce and its related areas to ensure greater growth in the country. One of the major problems affecting the agricultural produce of farmers worldwide is the unrestricted growth of weeds in the farm and agricultural areas which results in reduced produce for the farmers. One of the most elementary steps in the process of weed removal involves the detection of weeds in a field filled with agricultural produce. This process has been made easy by implementing the YOLOv8 process, which has produced great results ensuring easy detection of weeds and crops, making it easier and efficient for the farmers to increase and enhance their produce. YOLOv8 offers improved weed and crop detection with a precise classification rate of 0.9895 which indicates a highly accurate and successful classification. This allows farmers to efficiently identify and eliminate weeds, leading to higher productivity and better crop yields, ultimately supporting the agricultural growth of the country. This model can ensure easier, more efficient, and enhanced detection to improve the process of identifying the weeds and thereby eliminating them.
https://ror.org/05njtdr40 
