Development of remote sensing techniques for the implementation of site-specific herbicide management

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dc.contributor.authorEddy, Peter R.
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorPeddle, Derek Roland
dc.contributor.supervisorSmith, Anne
dc.date.accessioned2008-04-02T20:31:37Z
dc.date.available2008-04-02T20:31:37Z
dc.date.issued2007
dc.degree.levelMasters
dc.descriptionxii, 106 leaves : ill. (col. ill.) ; 29 cmen
dc.description.abstractSelective 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.identifier.urihttps://hdl.handle.net/10133/631
dc.language.isoen_USen
dc.publisherLethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2007en
dc.publisher.departmentGeographyen
dc.publisher.facultyFaculty of Arts and Scienceen
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en
dc.subjectAgriculture -- Remote sensingen
dc.subjectWeeds -- Remote sensingen
dc.subjectHerbicidesen
dc.subjectWeeds -- Controlen
dc.subjectDissertations, Academicen
dc.titleDevelopment of remote sensing techniques for the implementation of site-specific herbicide managementen
dc.typeThesisen
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