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A Fast and Efficient Shape Descriptor for an Advanced Weed Type Classification Approach

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 6 No. 3 (2016)

DOI
10.11591/ijece.v6i3.pp1168-1175
ISSN
2088-8708, 2088-8708
Published
2016-06-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

In weed management, the distinction between monocots and dicots species is an important issue. Indeed, the yield is much higher with the application of a selective treatment instead of using a broadcast herbicide overall the parcel. This article presents a fast shape descriptor designed to distinguish between these two families of weeds. The efficiency of the descriptor is evaluated by analyzing data with the pattern recognition process known as the discriminant factor analysis (DFA). Excellent results have been obtained in the differentiation between these two weed species

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
Rachid AgounounUniversité Moulay Ismail de Meknes8
Abdelhai RahmaniUniversité 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>A Fast and Efficient Shape Descriptor for an Advanced Weed Type Classification Approach</dc:title><dc:creator>Tannouche, Adil</dc:creator><dc:creator>Sbai, Khalid</dc:creator><dc:creator>Rahmoune, Miloud</dc:creator><dc:creator>Zoubir, Amine</dc:creator><dc:creator>Agounoune, Rachid</dc:creator><dc:creator>Saadani, Rachid</dc:creator><dc:creator>Rahmani, Abdelali</dc:creator><dc:description>&lt;jats:p&gt;In weed management, the distinction between monocots and dicots species is an important issue. Indeed, the yield is much higher with the application of a selective treatment instead of using a broadcast herbicide overall the parcel. This article presents a fast shape descriptor designed to distinguish between these two families of weeds. The efficiency of the descriptor is evaluated by analyzing data with the pattern recognition process known as the discriminant factor analysis (DFA). Excellent results have been obtained in the differentiation between these two weed species&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2016-06-01</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.v6i3.pp1168-1175</dc:identifier><dc:identifier>10.11591/ijece.v6i3.pp1168-1175</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 6 No 3 (2016); 1168</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/472/346</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>