Computer vision based weed detection framework in agricultural landscapes | [Second International Conference on Computing Technologies, Tools and Applications (ICTAPP-24), June 4-6,2024 (Abstract Book) • 2024]

Author(s):

1. Nida Nizar: Department of Computer Systems Engineering, University of Engineering and Technology,Peshawar,Pakistan

2. Gul-e-Nayab: Department of Computer Systems Engineering, University of Engineering and Technology,Peshawar,Pakistan

3. Hafiz Qazi Ahmad Khan: Department of Computer Systems Engineering, University of Engineering and Technology,Peshawar,Pakistan

4. Muhammad Athar Javid Sethi: Department of Computer Systems Engineering, University of Engineering and Technology,Peshawar,Pakistan

5. Waseem Ullah Khan: Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture,Peshawar,

6. Noor Ul Arfeen: Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture,Peshawar,

Abstract:

The agricultural sector plays a pivotal role in economic growth and sustenance for numerous families worldwide. However, the presence of weeds poses a substantial threat, leading to environmental pollution and economic challenges. This paper delves into the development of an innovative weed detection system utilizing computer vision techniques. Traditional methods relying on uniform herbicide application across entire fields are not only inefficient but also contribute to environmental degradation. The proposed system integrates advanced computer vision methodologies, including You Only Look Once (YOLO) algorithm, to enable real-time, accurate weed detection in agricultural fields. By leveraging these technologies, the research aims to facilitate precise identification and classification of weeds, enabling targeted herbicide application. This approach not only minimizes environmental impact but also contributes to significant cost savings for farmers. The study aligns with the principles of precision agriculture, focusing on sustainability, reduced chemical usage, and enhanced crop yields through the application of intelligent computer vision technologies. Overall, this research strives to provide a robust solution for weed detection in agriculture, promoting environmental stewardship and economic efficiency.

Page(s): 1-1

DOI: DOI not available

Published: Journal: Second International Conference on Computing Technologies, Tools and Applications (ICTAPP-24), June 4-6,2024 (Abstract Book), Volume: 0, Issue: 0, Year: 2024

Keywords:

Agriculture , Weed , YOLO , Herbicide

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