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Applied Spectroscopy

Applied Spectroscopy

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  • Vol. 67, Iss. 4 — Apr. 1, 2013
  • pp: 463–469

Detection of Huanglongbing-Infected Citrus Leaves Using Statistical Models with a Fluorescence Sensor

Sindhuja Sankaran and Reza Ehsani

Applied Spectroscopy, Vol. 67, Issue 4, pp. 463-469 (2013)


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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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Citation
Sindhuja Sankaran and Reza Ehsani, "Detection of Huanglongbing-Infected Citrus Leaves Using Statistical Models with a Fluorescence Sensor," Appl. Spectrosc. 67, 463-469 (2013)
http://www.opticsinfobase.org/as/abstract.cfm?URI=as-67-4-463


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