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Rachid Agounoun
Université Moulay Ismail, FS-EST, Laboratoire d’Etude des Matériaux Avancés et Applications, Meknes, Morocco
Computer Science & IT Electrical & Electronics Engineering

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Real Time Weed Detection using a Boosted Cascade of Simple Features Adil Tannouche; Khalid Sbai; Miloud Rahmoune; Rachid Agounoun; Abdelhai Rahmani; Abdelali Rahmani
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (752.381 KB) | DOI: 10.11591/ijece.v6i6.pp2755-2765

Abstract

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.
Co-Authors Abdelali Rahmani Abdelhai Rahmani Adil Tannouche Khalid Sbai Miloud Rahmoune