{"id":{"repo_id":"lethbridge","oai_identifier":"oai:opus.uleth.ca:10133/631"},"canonical_url":"https://search.dev.ndltd.org/etd/lethbridge/oai:opus.uleth.ca:10133/631","repository":{"repo_id":"lethbridge","name":"University of Lethbridge","base_url":"https://opus.uleth.ca/server/oai/request"},"display":{"title":"Development of remote sensing techniques for the implementation of site-specific herbicide management","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Eddy, Peter R.","University of Lethbridge. Faculty of Arts and Science"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2007,"date_issued":"2007","date_published":"2007","updated_at":"2026-07-27T20:02:29Z","subjects":["Agriculture -- Remote sensing","Weeds -- Remote sensing","Herbicides","Weeds -- Control","Dissertations, Academic"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10133/631"],"render_values":[{"text":"hdl:10133/631","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2007"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Agriculture -- Remote sensing","Weeds -- Remote sensing","Herbicides","Weeds -- Control","Dissertations, Academic"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10133/631"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["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."]},{"key":"dc:title","label":"Title","values":["Development of remote sensing techniques for the implementation of site-specific herbicide management"]}]}],"canonical_facts":{"dc:date.issued":["2007"],"dc:description.other":["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."],"dc:identifier":["hdl:10133/631"],"dc:subject":["Agriculture -- Remote sensing","Weeds -- Remote sensing","Herbicides","Weeds -- Control","Dissertations, Academic"],"dc:title":["Development of remote sensing techniques for the implementation of site-specific herbicide management"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:02:29Z"}