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Real Time Weed Detection using a Boosted Cascade of Simple Features

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 6 No. 6 (2016)

DOI
10.11591/ijece.v6i6.11878
ISSN
2088-8708, 2088-8708
Published
2016-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc-nd/4.0

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.

Collaboration network of the authors

14 authors · 39 co-authorship links · 5 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Université Moulay Ismail de Meknes
  • Université Sultan Moulay Slimane
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Amine ZoubirUniversité Moulay Ismail de Meknes8
Rachid AgounouneUniversité Moulay Ismail de Meknes8
Rachid SaadaniUniversité Moulay Ismail de Meknes8
Ahmed GagaUniversité Sultan Moulay Slimane1
Mohammed BoutallineUniversité Sultan Moulay Slimane1
Soufiane BelhouidegUniversité Sultan Moulay Slimane1
Aafaf BeljadidUniversité Sultan Moulay Slimane1
Abdessamad BaloukiUniversité Sultan Moulay Slimane1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref 
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Real Time Weed Detection using a Boosted Cascade of Simple Features</dc:title><dc:creator>Tannouche, Adil</dc:creator><dc:creator>Sbai, Khalid</dc:creator><dc:creator>Rahmoune, Miloud</dc:creator><dc:creator>Agounoun, Rachid</dc:creator><dc:creator>Rahmani, Abdelhai</dc:creator><dc:creator>Rahmani, Abdelali</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;p&amp;gt;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.&amp;lt;/p&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2016-12-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v6i6.11878</dc:identifier><dc:identifier>10.11591/ijece.v6i6.11878</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 6 No 6 (2016); 2755</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://www.iaescore.com/journals/index.php/IJECE/article/viewFile/5887/5547</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0</dc:rights></oai_dc:dc>