Tannouche, A. and Sbai, K. and Rahmoune, M. and Agounoune, R. and Rahmani, A. and Rahmani, A. (2016) Real time weed detection using a boosted cascade of simple features. International Journal of Electrical and Computer Engineering, 6 (6). pp. 2755-2765.

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Weed detection is a crucial issue in precision agriculture. In computer vision, variety of techniques are developed to detect, identify and locate weeds in different cultures. In this article, we present a real-time new weed detection method, through an embedded monocular vision. Our approach is based on the use of a cascade of discriminative classifiers formed by the Haar-like features. The quality of the results determines the validity of our approach, and opens the way to new horizons in weed detection. Copyright © 2016 Institute of Advanced Engineering and Science. All rights reserved.

Item Type: Article
Subjects: Computer Science
Divisions: SCIENTIFIC PRODUCTION > Computer Science
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:46
URI: http://eprints.umi.ac.ma/id/eprint/2616

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