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Citrus Huanglongbing Detection Using Narrow-Band Imaging and Polarized Illumination
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Transactions of the ASABE. 57(1): 259-272. (doi: 10.13031/trans.57.10147) @2014
Authors: Alireza Pourreza, Won Suk Lee, Eran Raveh, Reza Ehsani, Edgardo Etxeberria
Keywords: Classification, HLB, Image analysis, Starch concentration, Textural features.
The insect-spread bacterial infection known as citrus greening or Huanglongbing (HLB) is a very destructive citrus disease and has caused massive losses in Florida’s citrus industry. Early, easy, and less expensive HLB detection based on particular symptoms, such as starch accumulation in the citrus leaf, would increase the chance of preventing the disease from being spread and causing more damage. The ability of narrow-band imaging and polarizing filters in detecting starch accumulation in symptomatic citrus leaf was evaluated in this study. A custom-made image acquisition system was developed for this purpose in which leaf samples were illuminated with polarized light using narrow-band high-power LEDs at 400 nm and 591 nm, and the reflectance was measured by two monochrome cameras. Two polarizing filters were mounted in perpendicular directions in front of the cameras so that each camera acquired an image with reflected light in only one direction (parallel or perpendicular to the illumination polarization). Four groups of textural features, including gray, local binary pattern, local similarity pattern, and gray-level co-occurrence features, were extracted and ranked using several feature selection methods. Seven classifiers (support vector machine, linear, naive Bayes linear, quadratic, naive Bayes quadratic, Mahalanobis, and k nearest neighbor) were evaluated, and the best classifiers and sets of features were selected based on their accuracy. The leaf samples were collected from the ‘Hamlin’ and ‘Valencia’ varieties of citrus. Three classes of samples (magnesium-deficient, HLB-positive zinc-deficient, and HLB-negative zinc-deficient) were considered in the classification process to confirm the starch detection ability of the system. Overall average accuracies of 93.1% and 89.6% in HLB detection were obtained for the ‘Hamlin’ and ‘Valencia’ varieties, respectively, using a step-by-step classification method. The results of this study showed that the starch accumulation in HLB-symptomatic leaves rotated the polarization planar of light at 591 nm, and this property can be effectively used in a fast and inexpensive HLB detection system.(Download PDF) (Export to EndNotes)