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dc.contributor.supervisor Peddle, Derek Roland
dc.contributor.supervisor Smith, Anne Eddy, Peter R. University of Lethbridge. Faculty of Arts and Science 2008-04-02T20:31:37Z 2008-04-02T20:31:37Z 2007
dc.description xii, 106 leaves : ill. (col. ill.) ; 29 cm en
dc.description.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. en
dc.language.iso en_US en
dc.publisher Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2007 en
dc.relation.ispartofseries Thesis (University of Lethbridge. Faculty of Arts and Science) en
dc.subject Agriculture -- Remote sensing en
dc.subject Weeds -- Remote sensing en
dc.subject Herbicides en
dc.subject Weeds -- Control en
dc.subject Dissertations, Academic en
dc.title Development of remote sensing techniques for the implementation of site-specific herbicide management en
dc.type Thesis en
dc.publisher.faculty Faculty of Arts and Science en
dc.publisher.department Geography en Masters

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