Work

AI-Based Weed Detection Algorithm using YOLOv8

Year: 2025

Type: conference-paper

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 mAP50-95, 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.

Source: Proceedings of International Conference on Artificial Life and Robotics

DOI: 10.5954/icarob.2025.os2-2

Authors: Renuka Devi Rajagopal, Rethvik Menon C, T S PradeepKumar, Heshalini Rajagopal

Institutions: Vellore Institute of Technology University, Centre Universitaire de Mila, MILA University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: bronze

OpenAlex ID: W4409913525