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.
Vol. 8, Iss. 5 Virtual Journal for Biomedical Optics
Sindhuja Sankaran and Reza Ehsani, "Detection of Huanglongbing-Infected Citrus Leaves Using Statistical Models with a Fluorescence Sensor," Appl. Spectrosc. 67, 463-469 (2013)
References are not available for this paper.