Journal article
Detection of Huanglongbing-Infected Citrus Leaves Using Statistical Models with a Fluorescence Sensor
Applied spectroscopy, Vol.67(4), pp.463-469
04/2013
Handle:
https://hdl.handle.net/2376/107539
PMID: 23601547
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.
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Details
- Title
- Detection of Huanglongbing-Infected Citrus Leaves Using Statistical Models with a Fluorescence Sensor
- Creators
- Sindhuja Sankaran - Citrus Research and Education Center, IFAS, University of Florida, 700 Experiment Station Road, Lake Alfred, FL-33880, USAReza Ehsani - Citrus Research and Education Center, IFAS, University of Florida, 700 Experiment Station Road, Lake Alfred, FL-33880, USA
- Publication Details
- Applied spectroscopy, Vol.67(4), pp.463-469
- Academic Unit
- Biological Systems Engineering, Department of
- Publisher
- SAGE Publications; London, England
- Identifiers
- 99900546763701842
- Language
- English
- Resource Type
- Journal article