The agriculture industry experiences severe economic losses each year when wheat crops become infected with Fusarium and the mycotoxin Deoxynivalenol (DON). This research investigated the feasibility of using near infrared hyperspectral imaging to detect Fusarium damage and DON in Canadian Western Red Spring wheat. Four samples were selected from each grain grade resulting in 16 samples and 240 hyperspectral data cubes. The data cubes were calibrated to the system, the consistent spectra was found and then a 1- nearest neighbour classifier was generated. Grade percentages were computed and used to generate two 3- nearest neighbour classifiers, one for identifying Fusarium damage and the other for identifying DON content. The Fusarium damage classifier had an accuracy of 85% and the DON content classifier had an accuracy of 80%. While a single sample image classification will not replace manual testing, the use of multiple samples from one harvest could reduce manual inspections.
| Identifer | oai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/30189 |
| Date | 09 January 2015 |
| Creators | Brown, Jennifer |
| Contributors | Paliwal, Jitendra (Biosystems Engineering) Morrison, Jason (Biosystems Engineering), Hewko, Mark (Biosystems Engineering) Fernando, Dilantha (Plant Science) |
| Source Sets | University of Manitoba Canada |
| Detected Language | English |
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