首页    期刊浏览 2024年11月30日 星期六
登录注册

文章基本信息

  • 标题:Real Time Weed Detection using a Boosted Cascade of Simple Features
  • 本地全文:下载
  • 作者:Adil Tannouche ; Khalid Sbai ; Miloud Rahmoune
  • 期刊名称:International Journal of Electrical and Computer Engineering
  • 电子版ISSN:2088-8708
  • 出版年度:2016
  • 卷号:6
  • 期号:6
  • 页码:2755-2765
  • DOI:10.11591/ijece.v6i6.11878
  • 语种:English
  • 出版社:Institute of Advanced Engineering and Science (IAES)
  • 摘要: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.
  • 其他摘要: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.
  • 关键词:Artificial vision;AdaBoost algorithm;Haar-like features;Weed detection;Precision agriculture
国家哲学社会科学文献中心版权所有