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
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.
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