Detecting Sugarcane yellow leaf virus infection in asymptomatic leaves with hyperspectral remote sensing and associated leaf pigment changes

  • Grisham M
  • Johnson R
  • Zimba P
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Abstract

Sugarcane infected with Sugarcane yellow leaf virus (SCYLV) rarely produces visual symptoms until late in the growing season. High-resolution, hyperspectral reflectance data from SCYLV-infected and non-infected leaves of two cultivars, LCP 85-384 and Ho 95-988, were measured and analyzed on 13 July, 12 October, and 4 November 2005. All plants were asymptomatic. Infection was determined by reverse transcriptase-polymerase chain reaction (RT-PCR) analysis. Results from discriminant analysis showed that leaf reflectance was effective at predicting SCYLV infection in 73% of the cases in both cultivars using resubstitution and 63% and 62% in LCP 85-384 and Ho 95-988, respectively, using cross-validation. Predictive equations were improved when data from sampling dates were analyzed individually. SCYLV infection influenced the concentration of several leaf pigments including violaxanthin, β-carotene, neoxanthin, and chlorophyll a. Pigment data were effective at predicting SCYLV infection in 80% of the samples in the combined data set using the derived discriminant function with resubstitution, and 71% with cross-validation. Although further research is needed to improve the accuracy of the predictive equations, the results of this study demonstrate the potential application of hyperspectral remote sensing as a rapid, field-based method of identifying SCYLV-infected sugarcane plants prior to symptom expression. © 2010.

Author-supplied keywords

  • Hyperspectral
  • Leaf pigments
  • Remote sensing
  • Sugarcane
  • Sugarcane yellow leaf virus

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Authors

  • Michael P. Grisham

  • Richard M. Johnson

  • Paul V. Zimba

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