Abstract
Raman spectroscopy (RS) is an emerging analytical technique that can be used to develop and deploy precision agriculture. RS allows for confirmatory diagnostic of biotic and abiotic stresses on plants. Specifically, RS can be used for Huanglongbing (HLB) diagnostics on both orange and grapefruit trees, as well as detection and identification of various fungal and viral diseases. The questions that remain to be answered is how early can RS detect and identify the disease and whether RS is more sensitive than qPCR, the "golden standard" in pathogen diagnostics? Using RS and HLB as case study, we monitored healthy (qPCR-negative) in-field grown citrus trees and compared their spectra to the spectra collected from healthy orange and grapefruit trees grown in a greenhouse with restricted insect access and confirmed as HLB free by qPCR. Our result indicated that RS was capable of early prediction of HLB and that nearly all in-field qPCR-negative plants were infected by the disease. Using advanced multivariate statistical analysis, we also showed that qPCR-negative plants exhibited HLB-specific spectral characteristics that can be distinguished from unrelated nutrition deficit characteristics. These results demonstrate that RS is capable of much more sensitive diagnostics of HLB compared to qPCR.