Detecting Sugarcane yellow leaf virus infection in asymptomatic leaves with hyperspectral remote sensing and...

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, beta-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.