WAVELENGTH AND VISUAL FEATURE SELECTION FOR NON-DESTRUCTIVE EVALUATION OF FUSARIUM HEAD BLIGHT IN WHEAT USING HYPERSPECTRAL IMAGING

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.

Name thesis:degree_name

Master of Science (M.Sc.)

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Saskatchewan

Year dc:date.issued

2026

Author dc:creator

• Tingstad, Grant D

Advisor dc:contributor.advisor

• Noble, Scott D

Committee members dc:contributor.committeemember

• Noble, Scott D
• Kutcher, Hadley R
• Wiens, Travis

• 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

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10388/17996

OAI identifier oai:identifier

oai:harvest.usask.ca:10388/17996