{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/52563"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/52563","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Agricultural Crop Monitoring with Computer Vision","abstract":"Precision agriculture allows farmers to efficiently use their resources with site-specific applications. The current work looks to computer vision for the data collection method necessary for such a smart field, including cameras sensitive to visual (430-650~nm), near infrared (NIR,750-900~nm), shortwave infrared (SWIR,950-1700~nm), and longwave infrared (LWIR,7500-16000~nm) light. Three areas are considered in the study: image segmentation, multispectral image registration, and the feature tracking of a stressed plant. The accuracy of several image segmentation methods are compared. Basic thresholding on pixel intensities and vegetation indices result in accuracies below 75% . Neural networks (NNs) and support vector machines (SVMs) label correctly at 89% and 79%, respectively, when given only visual information, and final accuracies of 97% when the near infrared is added. The point matching methods of Scale Invariant Feature Transform (SIFT) and Edge Orient Histogram (EOH) are compared for accuracy. EOH improves the matching accuracy, but ultimately not enough for the current work. In order to track the image features of a stressed plant, a set of basil and catmint seedlings are grown and placed under drought and hypoxia conditions. Trends are shown in the average pixel values over the lives of the plants and with the vegetation indices, especially that of Marchant and NIR. Lastly, trends are seen in the image textures of the plants through use of textons.","abstract_html":"Precision agriculture allows farmers to efficiently use their resources with site-specific applications. The current work looks to computer vision for the data collection method necessary for such a smart field, including cameras sensitive to visual (430-650~nm), near infrared (NIR,750-900~nm), shortwave infrared (SWIR,950-1700~nm), and longwave infrared (LWIR,7500-16000~nm) light. Three areas are considered in the study: image segmentation, multispectral image registration, and the feature tracking of a stressed plant. The accuracy of several image segmentation methods are compared. Basic thresholding on pixel intensities and vegetation indices result in accuracies below 75% . Neural networks (NNs) and support vector machines (SVMs) label correctly at 89% and 79%, respectively, when given only visual information, and final accuracies of 97% when the near infrared is added. The point matching methods of Scale Invariant Feature Transform (SIFT) and Edge Orient Histogram (EOH) are compared for accuracy. EOH improves the matching accuracy, but ultimately not enough for the current work. In order to track the image features of a stressed plant, a set of basil and catmint seedlings are grown and placed under drought and hypoxia conditions. Trends are shown in the average pixel values over the lives of the plants and with the vegetation indices, especially that of Marchant and NIR. Lastly, trends are seen in the image textures of the plants through use of textons.","abstract_has_math":false,"creators":["Burns, James Ian"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Mechanical Engineering","degree_department":"Mechanical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Wicks, Alfred L."],"committee_members":["Bird, John P.","Woolsey, Craig A."],"year":2014,"date_issued":"2014-09-25","date_published":"2014-09-25","updated_at":"2026-07-22T22:20:12Z","subjects":["Computer Vision","Machine learning","Agriculture","Multispectral","Automation"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:3816"],"render_values":[{"text":"vt_gsexam:3816","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/52563","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Wicks, Alfred L."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Bird, John P.","Woolsey, Craig A."]},{"key":"dc:contributor.department","label":"Department","values":["Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["Burns, James Ian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2015-05-23T08:04:40Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2015-05-23T08:04:40Z"]},{"key":"dc:date.issued","label":"Date","values":["2014-09-25"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Vision","Machine learning","Agriculture","Multispectral","Automation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:3816"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/52563"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Precision agriculture allows farmers to efficiently use their resources with site-specific applications. The current work looks to computer vision for the data collection method necessary for such a smart field, including cameras sensitive to visual (430-650~nm), near infrared (NIR,750-900~nm), shortwave infrared (SWIR,950-1700~nm), and longwave infrared (LWIR,7500-16000~nm) light. Three areas are considered in the study: image segmentation, multispectral image registration, and the feature tracking of a stressed plant. The accuracy of several image segmentation methods are compared. Basic thresholding on pixel intensities and vegetation indices result in accuracies below 75% . Neural networks (NNs) and support vector machines (SVMs) label correctly at 89% and 79%, respectively, when given only visual information, and final accuracies of 97% when the near infrared is added. The point matching methods of Scale Invariant Feature Transform (SIFT) and Edge Orient Histogram (EOH) are compared for accuracy. EOH improves the matching accuracy, but ultimately not enough for the current work. In order to track the image features of a stressed plant, a set of basil and catmint seedlings are grown and placed under drought and hypoxia conditions. Trends are shown in the average pixel values over the lives of the plants and with the vegetation indices, especially that of Marchant and NIR. Lastly, trends are seen in the image textures of the plants through use of textons."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Agricultural Crop Monitoring with Computer Vision"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Wicks, Alfred L."],"dc:contributor.committeemember":["Bird, John P.","Woolsey, Craig A."],"dc:contributor.department":["Mechanical Engineering"],"dc:creator":["Burns, James Ian"],"dc:date.accessioned":["2015-05-23T08:04:40Z"],"dc:date.available":["2015-05-23T08:04:40Z"],"dc:date.issued":["2014-09-25"],"dc:description.abstract":["Precision agriculture allows farmers to efficiently use their resources with site-specific applications. The current work looks to computer vision for the data collection method necessary for such a smart field, including cameras sensitive to visual (430-650~nm), near infrared (NIR,750-900~nm), shortwave infrared (SWIR,950-1700~nm), and longwave infrared (LWIR,7500-16000~nm) light. Three areas are considered in the study: image segmentation, multispectral image registration, and the feature tracking of a stressed plant. The accuracy of several image segmentation methods are compared. Basic thresholding on pixel intensities and vegetation indices result in accuracies below 75% . Neural networks (NNs) and support vector machines (SVMs) label correctly at 89% and 79%, respectively, when given only visual information, and final accuracies of 97% when the near infrared is added. The point matching methods of Scale Invariant Feature Transform (SIFT) and Edge Orient Histogram (EOH) are compared for accuracy. EOH improves the matching accuracy, but ultimately not enough for the current work. In order to track the image features of a stressed plant, a set of basil and catmint seedlings are grown and placed under drought and hypoxia conditions. Trends are shown in the average pixel values over the lives of the plants and with the vegetation indices, especially that of Marchant and NIR. Lastly, trends are seen in the image textures of the plants through use of textons."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:3816"],"dc:identifier.uri":["http://hdl.handle.net/10919/52563"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Computer Vision","Machine learning","Agriculture","Multispectral","Automation"],"dc:title":["Agricultural Crop Monitoring with Computer Vision"],"dc:type":["Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:12Z"}