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.
Highlights
Optical sensing has been widely used in agricultural applications, such as food quality control and plant disease and stress detection [1,2,3]
As outlined in Materials and Methods (Data Collection), six different class labels for individual leaves were defined based on the level of Candidatus Liberibacter asiaticus (CLas) infection, as defined by CLas amplicon copy numbers (Table 2)
The seedling infection protocol proved inefficient in achieving high titers of bacteria in individual leaves, on a seedling basis, approximately 85% were positive for CLas at some level
Summary
Optical sensing has been widely used in agricultural applications, such as food quality control and plant disease and stress detection [1,2,3]. These approaches have predominantly used fluorescence, reflectance, and, recently, polarized imaging to assess the physiological and disease status of leaves and fruits. Light first encounters the epidermal layer of the plant, which serves to protect the photosynthetic apparatus from the harmful effects of UV. Photoexcitation results in fluorescence emissions from various substances in the epidermal layer, such as phenolics and flavonoids [3]. Various pigments including carotenoids and Robotics 2017, 6, 11; doi:10.3390/robotics6020011 www.mdpi.com/journal/robotics
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