Detection of Fungal Infection in Canola using Near-Infrared Hyperspectral Imaging

T. Senthilkumar; C.B. Singh; D.S. Jayas; N.D.G. White

doi:10.52151/jae2012491.1464

Authors

T. Senthilkumar Graduate Research Assistant, Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, R3T 5V6 Author
C.B. Singh Post-Doctoral Fellow, Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, R3T 5V6 Canada Author
D.S. Jayas Vice-President (Research and International), University of Manitoba, Winnipeg, MB, R3T 2N2 Canada Author
N.D.G. White Research Scientist, Agriculture and Agri-food Canada, Cereal Research Centre, Winnipeg, MB, R3T 2M9 Canada Author

DOI:

https://doi.org/10.52151/jae2012491.1464

Keywords:

Hyperspectral imaging , NIR , Fungal infection, Canola

Abstract

Near-infrared (NIR) hyperspectral imaging was used to detect the presence of fungal infection in stored canola. Artificially fungal infected (Aspergillus glaucus group) canola was subjected to single kernel imaging every two weeks after incubation using an NIR imaging system in the wavelength range of 1000 to 1600 nm at 60 evenly distributed wavelengths. Three wavelengths 1100, 1230 and 1300 nm were identified as significant wavelengths and used in the analysis. Statistical discriminant classifiers (Linear and Quadratic) were used to classify healthy, two–, four–, six–, eight–, and ten–week fungal incubated samples. The linear and quadratic statistical classifiers gave maximum accuracy of 99% for healthy samples and 100% for fungal infected samples at later stages of infection levels and 90 to 95% for the first four weeks of A. glaucus infected samples.

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