{"id":{"repo_id":"sask","oai_identifier":"oai:harvest.usask.ca:10388/17996"},"canonical_url":"https://search.dev.ndltd.org/etd/sask/oai:harvest.usask.ca:10388/17996","repository":{"repo_id":"sask","name":"University of Saskatchewan","base_url":"https://harvest.usask.ca/server/oai/request"},"display":{"title":"WAVELENGTH AND VISUAL FEATURE SELECTION FOR NON-DESTRUCTIVE EVALUATION OF FUSARIUM HEAD BLIGHT IN WHEAT USING HYPERSPECTRAL IMAGING","abstract":"Fusarium head blight (FHB) is a fungal disease affecting the heads of cereal crops and has caused significant economic losses in the Canadian prairie wheat industry. Although wheat breeding efforts to enhance resistance genes are ongoing, efficient and accurate detection of FHB remains critical for breeding programs and crop protection. This thesis presents the development of a custom hyperspectral scanner optimized for seed and kernel data acquisition and evaluates an extensive range of classification models trained on spatial–spectral features. The scanner captured hyperspectral scans of over 1,500 healthy and Fusarium-damaged kernels, from which statistical features were extracted at each wavelength. Classification models were trained to assess the contribution of individual wavelengths and statistical features to overall performance, and an exhaustive combinatorial analysis was conducted to identify high-performing feature pairs. The best-performing model achieved an accuracy of 99.7% using a large feature set. Lightweight models identified through feature analysis achieved accuracies of up to 97.3% and correctly classified up to 99.4% of Fusarium-damaged kernels using as few as two features.","abstract_html":"Fusarium head blight (FHB) is a fungal disease affecting the heads of cereal crops and has caused significant economic losses in the Canadian prairie wheat industry. Although wheat breeding efforts to enhance resistance genes are ongoing, efficient and accurate detection of FHB remains critical for breeding programs and crop protection. This thesis presents the development of a custom hyperspectral scanner optimized for seed and kernel data acquisition and evaluates an extensive range of classification models trained on spatial–spectral features. The scanner captured hyperspectral scans of over 1,500 healthy and Fusarium-damaged kernels, from which statistical features were extracted at each wavelength. Classification models were trained to assess the contribution of individual wavelengths and statistical features to overall performance, and an exhaustive combinatorial analysis was conducted to identify high-performing feature pairs. The best-performing model achieved an accuracy of 99.7% using a large feature set. Lightweight models identified through feature analysis achieved accuracies of up to 97.3% and correctly classified up to 99.4% of Fusarium-damaged kernels using as few as two features.","abstract_has_math":false,"creators":["Tingstad, Grant D"],"institution":"University of Saskatchewan","degree_name":"Master of Science (M.Sc.)","degree_level":"Masters","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Noble, Scott D"],"committee_chairs":[],"committee_members":["Noble, Scott D","Kutcher, Hadley R","Wiens, Travis"],"year":2026,"date_issued":"2026-02-24","date_published":"2026-02-24","updated_at":"2026-07-24T04:26:54Z","subjects":["Fusarium head blight","FHB","Fusarium-damaged kernels","FDK","Wheat phenotyping","Hyperspectral imaging","Hyperspectral scanner","Kernel analysis","Spectral-spatial features","Feature extraction","Feature selection","Machine learning classification","Disease detection","Precision agriculture"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10388/17996","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Noble, Scott D"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Noble, Scott D","Kutcher, Hadley R","Wiens, Travis"]},{"key":"dc:creator","label":"Author","values":["Tingstad, Grant D"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-02-24T14:58:07Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-02-24T14:58:07Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-02-24"]},{"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 (M.Sc.)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Saskatchewan"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Fusarium head blight","FHB","Fusarium-damaged kernels","FDK","Wheat phenotyping","Hyperspectral imaging","Hyperspectral scanner","Kernel analysis","Spectral-spatial features","Feature extraction","Feature selection","Machine learning classification","Disease detection","Precision agriculture"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10388/17996"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Fusarium head blight (FHB) is a fungal disease affecting the heads of cereal crops and has caused significant economic losses in the Canadian prairie wheat industry. Although wheat breeding efforts to enhance resistance genes are ongoing, efficient and accurate detection of FHB remains critical for breeding programs and crop protection. This thesis presents the development of a custom hyperspectral scanner optimized for seed and kernel data acquisition and evaluates an extensive range of classification models trained on spatial–spectral features. The scanner captured hyperspectral scans of over 1,500 healthy and Fusarium-damaged kernels, from which statistical features were extracted at each wavelength. Classification models were trained to assess the contribution of individual wavelengths and statistical features to overall performance, and an exhaustive combinatorial analysis was conducted to identify high-performing feature pairs. The best-performing model achieved an accuracy of 99.7% using a large feature set. Lightweight models identified through feature analysis achieved accuracies of up to 97.3% and correctly classified up to 99.4% of Fusarium-damaged kernels using as few as two features."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["WAVELENGTH AND VISUAL FEATURE SELECTION FOR NON-DESTRUCTIVE EVALUATION OF FUSARIUM HEAD BLIGHT IN WHEAT USING HYPERSPECTRAL IMAGING"]}]}],"canonical_facts":{"dc:contributor.advisor":["Noble, Scott D"],"dc:contributor.committeemember":["Noble, Scott D","Kutcher, Hadley R","Wiens, Travis"],"dc:creator":["Tingstad, Grant D"],"dc:date.accessioned":["2026-02-24T14:58:07Z"],"dc:date.available":["2026-02-24T14:58:07Z"],"dc:date.issued":["2026-02-24"],"dc:description.abstract":["Fusarium head blight (FHB) is a fungal disease affecting the heads of cereal crops and has caused significant economic losses in the Canadian prairie wheat industry. Although wheat breeding efforts to enhance resistance genes are ongoing, efficient and accurate detection of FHB remains critical for breeding programs and crop protection. This thesis presents the development of a custom hyperspectral scanner optimized for seed and kernel data acquisition and evaluates an extensive range of classification models trained on spatial–spectral features. The scanner captured hyperspectral scans of over 1,500 healthy and Fusarium-damaged kernels, from which statistical features were extracted at each wavelength. Classification models were trained to assess the contribution of individual wavelengths and statistical features to overall performance, and an exhaustive combinatorial analysis was conducted to identify high-performing feature pairs. The best-performing model achieved an accuracy of 99.7% using a large feature set. Lightweight models identified through feature analysis achieved accuracies of up to 97.3% and correctly classified up to 99.4% of Fusarium-damaged kernels using as few as two features."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10388/17996"],"dc:language.iso":["en"],"dc:subject":["Fusarium head blight","FHB","Fusarium-damaged kernels","FDK","Wheat phenotyping","Hyperspectral imaging","Hyperspectral scanner","Kernel analysis","Spectral-spatial features","Feature extraction","Feature selection","Machine learning classification","Disease detection","Precision agriculture"],"dc:title":["WAVELENGTH AND VISUAL FEATURE SELECTION FOR NON-DESTRUCTIVE EVALUATION OF FUSARIUM HEAD BLIGHT IN WHEAT USING HYPERSPECTRAL IMAGING"],"dc:type":["Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science (M.Sc.)"],"thesis:institution_name":["University of Saskatchewan"]},"updated_at":"2026-07-24T04:26:54Z"}