Hyperspectral imaging for differentiating glyphosate-resistant and glyphosate-susceptible Italian Ryegrass

Authors: Huang, Yanbo; LEE, MATTHEW - Mississippi State University; Nandula, Vijay; Reddy, Krishna

Submitted to: American Journal of Plant Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/18/2018
Publication Date: 6/21/2018
Citation: Huang, Y., Lee, M.A., Nandula, V.K., Reddy, K.N. 2018. Hyperspectral imaging for differentiating glyphosate-resistant and glyphosate-susceptible Italian Ryegrass. American Journal of Plant Sciences. 9:1467-1477.

Interpretive Summary: It is important to control glyphosate-resistant (GR) weeds for profitable crop production. Scientists at USDA ARS Crop Production Systems Research Unit, Stoneville, Mississippi have developed a hyperspectral imaging method to differentiate GR and glyphosate-susceptible (GS) Italian ryegrass. This method was developed based on our previous successful research to develop the hyperspectral imaging technique to differentiate GR and GS Palmer amaranth. Although the accuracy varied for the technique to differentiate GR and GS Italian ryegrass and GR and GS Palmer amaranth, the results of this study provides a rapid, non-destructive approach to differentiate between GR and GS Italian ryegrass for improved site-specific weed management.

Technical 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 varied from 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.