OS2-2 AI-Based Weed Detection Algorithm using YOLOv8

Title:	OS2-2 AI-Based Weed Detection Algorithm using YOLOv8
Publication:	ICAROB2025
Volume:	30
Pages:	67-70
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2025.OS2-2
Author(s):	Renuka Devi Rajagopal, Rethvik Menon C, T S PradeepKumar, Heshalini Rajagopal
Publication Date:	February 13, 2025
Keywords:	Agriculture, Artificial Intelligence, Weed Detection, YOLOv8
Abstract:	The development of a country relies heavily on agricultural produce and its related sectors. However, farmers face significant challenges due to the uncontrolled growth of weeds, which reduces their yield. Weed detection is a key step in the removal process, and advances in technology, such as the YOLOv8 model, have simplified this task. YOLOv8 offers improved weed and crop detection, with enhancements of 1.3% and 1.17% in mAP50 and mAP5095, respectively, over the previous YOLOv5 model. 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.
PDF File:	https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS2/OS2-2.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/