Publications - Faculty - Recently Submitted Faculty Publications 2010
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Ehsani - Transactions of the ASABE
Detection of Huanglongbing Disease in Citrus using Fluorescence Spectroscopy
Sindhuja Sankaran and Reza Ehsani
Abstract.Huanglongbing (HLB) is an important citrus disease greatly affecting the citrus production in Florida and other parts of the world. Early disease detection would control the spread of this disease through the application of suitable management measures. This study evaluates the application of fluorescence sensing for HLB detection of citrus leaves. A commercial handheld fluorescence sensor was used to collect yellow, red, and far-red fluorescence at ultraviolet (UV), blue, green, and red excitations from healthy, nutrient-deficient and HLB-infected leaves of two different sweet orange cultivars: Hamlin and Valencia. Evaluation of the fluorescence sensing was performed under laboratory (controlled) and field conditions. Naïve-Bayes and bagged decision tree classifiers were trained and tested to assess their performance in classifying the healthy and stressed (nutrient-deficient) leaves. Results revealed that the Naïve-Bayes classifier yielded high classification accuracy under laboratory conditions (higher than 85%); while bagged decision tree yielded high overall classification accuracy under both laboratory and field conditions (higher than 94%). The bagged decision tree performed better than Naïve-Bayes classifier resulting in higher classification accuracy, though the computation time was at-least 10 times faster than that of Naïve-Bayes classifier. Also, feature extraction using forward feature selection indicated that the fluorescence features such as yellow fluorescence (UV) and simple fluorescence ratio (green) contributed towards differentiating healthy leaves from nutrient-deficient and HLB-infected leaves
