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Robotics 2017, 6(2), 11; doi:10.3390/robotics6020011

Feasibility of Using the Optical Sensing Techniques for Early Detection of Huanglongbing in Citrus Seedlings

1
Kearney Agricultural Research & Extension Center, University of California, Division of Agriculture and Natural Resources, 9240 S. Riverbend Ave., Parlier, CA 93648, USA
2
Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
3
Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32611, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Qin Zhang and Manoj Karkee
Received: 6 January 2017 / Revised: 10 April 2017 / Accepted: 19 April 2017 / Published: 23 April 2017
(This article belongs to the Special Issue Agriculture Robotics)
View Full-Text   |   Download PDF [1593 KB, uploaded 23 April 2017]   |  

Abstract

A vision sensor was introduced and tested for early detection of citrus Huanglongbing (HLB). This disease is caused by the bacterium Candidatus Liberibacter asiaticus (CLas) and is transmitted by the Asian citrus psyllid. HLB is a devastating disease that has exerted a significant impact on citrus yield and quality in Florida. Unfortunately, no cure has been reported for HLB. Starch accumulates in HLB infected leaf chloroplasts, which causes the mottled blotchy green pattern. Starch rotates the polarization plane of light. A polarized imaging technique was used to detect the polarization-rotation caused by the hyper-accumulation of starch as a pre-symptomatic indication of HLB in young seedlings. Citrus seedlings were grown in a room with controlled conditions and exposed to intensive feeding by CLas-positive psyllids for eight weeks. A quantitative polymerase chain reaction was employed to confirm the HLB status of samples. Two datasets were acquired; the first created one month after the exposer to psyllids and the second two months later. The results showed that, with relatively unsophisticated imaging equipment, four levels of HLB infections could be detected with accuracies of 72%–81%. As expected, increasing the time interval between psyllid exposure and imaging increased the development of symptoms and, accordingly, improved the detection accuracy. View Full-Text
Keywords: citrus greening; HLB; optical sensing; polarized imaging; seedling; high-throughput phenotyping citrus greening; HLB; optical sensing; polarized imaging; seedling; high-throughput phenotyping
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pourreza, A.; Lee, W.S.; Czarnecka, E.; Verner, L.; Gurley, W. Feasibility of Using the Optical Sensing Techniques for Early Detection of Huanglongbing in Citrus Seedlings. Robotics 2017, 6, 11.

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