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Leveraging YOLOv9 in Agriculture: An Intelligent System for Efficient Weed Detection

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dc.contributor.author Bouacida, Imane
dc.date.accessioned 2025-03-17T10:13:43Z
dc.date.available 2025-03-17T10:13:43Z
dc.date.issued 2024
dc.identifier.uri http://depot.umc.edu.dz/handle/123456789/14530
dc.description.abstract 13 Corresponding author email : bouacidaimane1@gmail.com Leveraging YOLOv9 in Agriculture: An Intelligent System for Efficient Weed Detection Bouacida, Imane* 8 mai 1945 Guelma University Abstract This research focuses on weed detection using artificial intelligence (AI) in agriculture. The goal of the study is to develop an intelligent system capable of automatically detecting and classifying weeds in agricultural fields through image analysis. Traditional manual weed detection methods are time- consuming, costly, and prone to human error. By harnessing advances in machine learning, image processing, and deep learning, an AI-based system can provide accurate, real-time information on weed presence, enabling farmers to optimize agricultural production fr_FR
dc.title Leveraging YOLOv9 in Agriculture: An Intelligent System for Efficient Weed Detection fr_FR
dc.type Article fr_FR


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