Development of remote sensing techniques for the implementation of site-specific herbicide management
Date
2007
Authors
Eddy, Peter R.
University of Lethbridge. Faculty of Arts and Science
Journal Title
Journal ISSN
Volume Title
Publisher
Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2007
Abstract
Selective application of herbicide in agricultural cropping systems provides both economic and environmental benefits. Implementation of this technology requires knowledge of the location and density of weed species within a crop. In this study, two image classification techniques (Artificial Neural Networks (ANNs) and Maximum Likelihood Classification (MLC)) are compared for accuracy in weed/crop species discrimination. In the summer of 2005, high spatial resolution (1.25mm) ground-based hyperspectral image data were acquired over field plots of three crop species seeded with two weed species. Image data were segmented using a threshold technique to identify vegetation for classification. The ANNs consistently outperformed MLC in single-date and multitemporal classification accuracy. With advancements in imaging technology and computer processing speed, these network models would constitute an option for real-time detection and mapping of weeds for the implementation of site-specific herbicide management.
Description
xii, 106 leaves : ill. (col. ill.) ; 29 cm
Keywords
Agriculture -- Remote sensing , Weeds -- Remote sensing , Herbicides , Weeds -- Control , Dissertations, Academic