title: Detection of Huanglongbing-infected citrus leaves using statistical models with a fluorescence sensor.
abstract: A handheld fluorescence sensor was tested as a sensing tool to identify Huanglongbing (HLB), a citrus disease, in both symptomatic and asymptomatic stages. Features such as yellow, red, and far-red fluorescence at UV, blue, green, and red excitations, and other fluorescence ratios were acquired from the healthy and HLB-infected leaves of different cultivars. The classification studies were performed with these features as well as selective fluorescence features. Results indicated that the bagged decision tree classifier yielded 97% classification accuracy in case of the healthy and symptomatic samples. Although the asymptomatic samples from the HLB-infected trees could not be classified based on polymerase chain reaction (PCR) analysis results, the Naïve-Bayes classifier grouped most of the asymptomatic samples as HLB. We found that a few fluorescence features such as yellow fluorescence (UV), far-red fluorescence (UV), yellow to far red fluorescence (UV), simple fluorescence ratio (green), and yellow fluorescence (green) could result in classification accuracies similar to those of the entire dataset.
authors.affiliation: Citrus Research and Education Center, IFAS, University of Florida, 700 Experiment Station Road, Lake Alfred, FL-33880, USA.
authors.forename: Sindhuja
authors.initials: S
authors.lastname: Sankaran
authors.forename: Reza
authors.initials: R
authors.lastname: Ehsani
mesh_terms: D002957:Citrus / Q000737:chemistry / Q000382:microbiology*; D010935:Plant Diseases / Q000382:microbiology*; D018515:Plant Leaves / Q000737:chemistry / Q000382:microbiology; D025341:Principal Component Analysis; D012230:Rhizobiaceae / Q000302:isolation & purification; D013050:Spectrometry, Fluorescence / Q000379:methods*