{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/31819"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/31819","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"Intra-field Nitrogen Estimation for Wheat and Corn using Unmanned Aerial Vehicle-based and Satellite Multispectral Imagery, Plant Biophysical Variables, Field Properties, and Machine Learning Methods","abstract":"Management of nitrogen (N) fertilizers is an important agricultural practice and field of research to increase productivity, minimize environmental impacts and the cost of production. To apply N fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery, PlanetScope satellite imagery, vegetation indices (VI), crop height, leaf area index (LAI), field topographic metrics, and soil properties to predict canopy nitrogen weight (g/m2) of corn and wheat fields in southwestern Ontario, Canada. Random Forests (RF) and Support Vector Regression (SVR) machine learning models were tested with combinations of variable datasets and evaluated for accuracy of canopy nitrogen weight prediction. The results demonstrate that UAV and satellite-based prediction models including spectral variables, crop biophysical parameters, and field conditions can provide accurate and useful information for fertilizer management.","abstract_html":"Management of nitrogen (N) fertilizers is an important agricultural practice and field of research to increase productivity, minimize environmental impacts and the cost of production. To apply N fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery, PlanetScope satellite imagery, vegetation indices (VI), crop height, leaf area index (LAI), field topographic metrics, and soil properties to predict canopy nitrogen weight (g/m2) of corn and wheat fields in southwestern Ontario, Canada. Random Forests (RF) and Support Vector Regression (SVR) machine learning models were tested with combinations of variable datasets and evaluated for accuracy of canopy nitrogen weight prediction. The results demonstrate that UAV and satellite-based prediction models including spectral variables, crop biophysical parameters, and field conditions can provide accurate and useful information for fertilizer management.","abstract_has_math":false,"creators":["Yu, Jody Seymon"],"institution":"The University of Western Ontario","degree_name":"M Sc","degree_level":null,"degree_discipline":"Geography","degree_department":null,"school":null,"contributors":[],"advisors":["Wang, Jinfei","Leblon, Brigitte"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-11-19","date_published":"2021-11-19","updated_at":"2026-07-27T21:55:59Z","subjects":["unmanned aerial vehicle","satellite imagery","precision agriculture","nitrogen management","machine learning"],"languages":["en_ca"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14721/31819","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Wang, Jinfei","Leblon, Brigitte"]},{"key":"dc:creator","label":"Author","values":["Yu, Jody Seymon"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-10T19:22:59Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-10T19:22:59Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-11-19"]},{"key":"dc:publisher","label":"Institution","values":["The University of Western Ontario"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Geography"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M Sc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["unmanned aerial vehicle","satellite imagery","precision agriculture","nitrogen management","machine learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_ca"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/20.500.14721/31819"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."]},{"key":"dc:description.abstract","label":"Abstract","values":["Management of nitrogen (N) fertilizers is an important agricultural practice and field of research to increase productivity, minimize environmental impacts and the cost of production. To apply N fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery, PlanetScope satellite imagery, vegetation indices (VI), crop height, leaf area index (LAI), field topographic metrics, and soil properties to predict canopy nitrogen weight (g/m2) of corn and wheat fields in southwestern Ontario, Canada. Random Forests (RF) and Support Vector Regression (SVR) machine learning models were tested with combinations of variable datasets and evaluated for accuracy of canopy nitrogen weight prediction. The results demonstrate that UAV and satellite-based prediction models including spectral variables, crop biophysical parameters, and field conditions can provide accurate and useful information for fertilizer management."]},{"key":"dc:title","label":"Title","values":["Intra-field Nitrogen Estimation for Wheat and Corn using Unmanned Aerial Vehicle-based and Satellite Multispectral Imagery, Plant Biophysical Variables, Field Properties, and Machine Learning Methods"]}]}],"canonical_facts":{"dc:contributor.advisor":["Wang, Jinfei","Leblon, Brigitte"],"dc:creator":["Yu, Jody Seymon"],"dc:date.accessioned":["2025-07-10T19:22:59Z"],"dc:date.available":["2025-07-10T19:22:59Z"],"dc:date.issued":["2021-11-19"],"dc:description":["The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."],"dc:description.abstract":["Management of nitrogen (N) fertilizers is an important agricultural practice and field of research to increase productivity, minimize environmental impacts and the cost of production. To apply N fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery, PlanetScope satellite imagery, vegetation indices (VI), crop height, leaf area index (LAI), field topographic metrics, and soil properties to predict canopy nitrogen weight (g/m2) of corn and wheat fields in southwestern Ontario, Canada. Random Forests (RF) and Support Vector Regression (SVR) machine learning models were tested with combinations of variable datasets and evaluated for accuracy of canopy nitrogen weight prediction. The results demonstrate that UAV and satellite-based prediction models including spectral variables, crop biophysical parameters, and field conditions can provide accurate and useful information for fertilizer management."],"dc:identifier.uri":["https://hdl.handle.net/20.500.14721/31819"],"dc:language.iso":["en_ca"],"dc:publisher":["The University of Western Ontario"],"dc:subject":["unmanned aerial vehicle","satellite imagery","precision agriculture","nitrogen management","machine learning"],"dc:title":["Intra-field Nitrogen Estimation for Wheat and Corn using Unmanned Aerial Vehicle-based and Satellite Multispectral Imagery, Plant Biophysical Variables, Field Properties, and Machine Learning Methods"],"dc:type":["thesis"],"thesis:degree_discipline":["Geography"],"thesis:degree_name":["M Sc"]},"updated_at":"2026-07-27T21:55:59Z"}