Crowell, Olivia L2016-09-062016-09-062016-09-03https://hdl.handle.net/11299/182058Detecting disease in crops increases yields and reduces economic loss. Traditional methods detect plant disease severity by hand, but it is a slow, time-consuming, and subjective process. Because there are physical, chemical, and physiological changes in plants with diseases, current research focuses on optical imaging as a more useful technique for monitoring disease. This experiment uses hyperspectral imaging (HSI) to investigate its capability as a diagnostic tool for wheat leaf rust disease. HSI has the potential to create more efficient High Throughput Phenotyping (HTP) methods to assist wheat breeders in the selection of a more resistant wheat variety. To collect data for this purpose, a HSI camera was attached to a ground vehicle that scanned wheat plots on an experimental field in St. Paul a month after the fungus was inoculated into the plots. White panels were laid out into the field to normalize the radiance based on the irradiance. Preprocessing techniques converted the pixels from their digital number to reflectance, which measures any physiological changes. The average spectral signatures of pixels were then compared to scoring data on a spectrum of R (resistant) to S (susceptible). Other categories include MR (moderately resistant), MS (moderately susceptible), etc. However, the spectral signatures that matched with R, MR, MRMS, MS, S, etc. varied unreasonably. R and S spectral lines were very similar to each other and did not exist as a maximum and minimum. This study is inconclusive which could be due to the calibration process since light cannot be controlled perfectly. It could also be because of the lack of control with varied wheat during data collection times.enHyperspectral imagingleaf wheat rust diseasephenotypingScholarly Text or Essay