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Ashourloo, D., Mobasheri, M. and Huete, A. (2014) Developing Two Spectral Disease Indices for Detection of Wheat Leaf Rust (Puccinia triticina). Remote Sensing, 6, 4723-4740.
https://doi.org/10.3390/rs6064723

has been cited by the following article:

  • TITLE: Hyperspectral Imaging for Differentiating Glyphosate-Resistant and Glyphosate-Susceptible Italian Ryegrass

    AUTHORS: Yanbo Huang, Matthew A. Lee, Vijay K. Nandula, Krishna N. Reddy

    KEYWORDS: Hyperspectral Imaging, Glyphosate Resistance, Italian Ryegrass

    JOURNAL NAME: American Journal of Plant Sciences, Vol.9 No.7, June 21, 2018

    ABSTRACT: Glyphosate is widely used in row crop weed control programs of glyphosate-resistant (GR) crops. With the accumulation of glyphosate use, several weeds have evolved resistance to glyphosate. In order to control GR weeds for profitable crop production, it is critical to first identify them in crop fields. Conventional method for identifying GR weeds is destructive, tedious and labor-intensive. This study developed hyperspectral imaging for rapid sensing of Italian ryegrass (Lolium perenne ssp. multiflorum) plants to determine if each plant is GR or glyphosate-susceptible (GS). In image analysis, a set of sensitive spectral bands was determined using a forward selection algorithm by optimizing the area under the receiver operating characteristic between GR and GS plants. Then, the dimensionality of selected bands was reduced using linear discriminant analysis. At the end the maximum likelihood classification was conducted for plant sample differentiation of GR Italian ryegrass from GS ones. The results indicated that the overall classification accuracy is between 75% and 80%. Although the accuracy is lower than the classification of Palmer amaranth (Amaranthus palmeri S. Wats.) in our previous study, this study provides a rapid, non-destructive approach to differentiate between GR and GS Italian ryegrass for improved site-specific weed management.