Journal of Spectral Imaging,   Volume 10   Article ID a4   (2021)

Peer reviewed Paper

Hyperspectral reflectance for non-invasive early detection of black shank disease in flue-cured tobacco

  • Austin Hayes  
  • T. David Reed
Virginia Polytechnic Institute and State University, USA and Southern Piedmont Agricultural Research and Extension Center, Blackstone, VA, USA
[email protected]
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 Corresponding Author
Virginia Polytechnic Institute and State University, USA and Southern Piedmont Agricultural Research and Extension Center, Blackstone, VA, USA
[email protected]
 https://orcid.org/0000-0001-5888-3764
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Flue-cured tobacco (Nicotiana tabacum L.) is a high value-per-acre crop that is intensively managed to optimise the yield of high-quality cured leaf. A 15-day study assessed the potential of hyperspectral reflectance data for detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Hyperspectral reflectance data were taken from a commercial flue-cured tobacco field with a progressing black shank infestation. The effort encompassed two key objectives. First, develop hyperspectral indices and/or machine learning classification models capable of detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Second, evaluate the model’s ability to separate pre-symptomatic plants from healthy plants. Two hyperspectral indices were developed to detect black shank incidence based on differences in the spectral profiles of asymptomatic flue-cured tobacco plants compared to those with black shank symptoms. While one of the indices is a broad-band index and the other uses narrow wavelength values, the statistical difference between the two indices was not significant and both provided an accurate classification of symptomatic plants. Further analysis of the indices showed significant differences between the index values of healthy and symptomatic plants (α = 0.05). In addition, the indices were able to detect black shank symptoms pre-symptomatically (α = 0.09). Subspace linear discriminant analysis, a machine learning classification, was also used for prediction of black shank incidence with up to 85.7 % classification accuracy. The implications of using either spectral indices or machine learning for classification for future black shank research are discussed.

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