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.