Detection of Huanglongbing(citrus greening) based on hyperspectral image analysis and PCR

Huanglongbing (HLB, citrus greening) is one of the most serious quarantine diseases of citrus worldwide. To monitor in real-time, recognize diseased trees, and efficiently prevent and control HLB disease in citrus, it is necessary to develop a rapid diagnostic method to detect HLB infected plants without symptoms. This study used Newhall navel orange plants as the research subject, and collected normal color leaf samples and chlorotic leaf samples from a healthy orchard and an HLB-infected orchard, respectively. First, hyperspectral data of the upper and lower leaf surfaces were obtained, and then the polymerase chain reaction (PCR) was used to detect the HLB bacterium in each leaf. The PCR test results showed that all samples from the healthy orchard were negative, and a portion of the samples from the infected orchard were positive. According to these results, the leaf samples from the orchards were divided into disease-free leaves and HLB-positive leaves, and the least squares support vector machine recognition model was established based on the leaf hyperspectral reflectance. The effect on the model of the spectra obtained from the upper and lower leaf surfaces was investigated and different pretreatment methods were compared and analyzed. It was observed that the HLB recognition rate values of the calibration and validation sets based on upper leaf surface spectra under 9-point smoothing pretreatment were 100% and 92.5%, respectively. The recognition rate values based on lower leaf surface spectra under the second-order derivative pretreatment were also 100% and 92.5%, respectively. Both upper and lower leaf surface spectra were available for recognition of HLB-infected leaves, and the HLB PCR-positive leaves could be distinguished from the healthy by the hyperspectral modeling analysis. The results of this study show that early and nondestructive detection of HLBinfected leaves without symptoms is possible, which provides a basis for the hyperspectral diagnosis of citrus with HLB.

Rapid detection of chlorophyll content and distribution in citrus orchards based on low-altitude remote sensing and bio-sensors

The accuracy of detecting the chlorophyll content in the canopy and leaves of citrus plants based on sensors with different scales and prediction models was investigated for the establishment of an easy and highly-efficient real-time nutrition diagnosis technology in citrus orchards.The fluorescent values of leaves and canopy based on the Multiplex 3.6 sensor,canopy hyperspectral reflectance data based on the FieldSpec4 radiometer and spectral reflectance based on low-altitude multispectral remote sensing were collected from leaves of Shatang mandarin and then analyzed.Additionally,the associations of the leaf SPAD(soil and plant analyzer development)value with the ratio vegetation index(RVI)and normalized differential vegetation index(NDVI)were analyzed.The leaf SPAD value predictive model was established by means of univariate and multiple linear regressions and the partial least squares method.Variable distribution maps of the relative canopy chlorophyll content based on spectral reflectance in the orchard were automatically created.The results showed that the correlations of the SPAD values obtained from the Multiplex 3.6 sensor,FieldSpec4 radiometer and low-altitude multispectral remote sensing were highly significant.The measures of goodness of fit of the predictive models were R^(2)=0.7063,RMSECV=3.7892,RE=5.96%,and RMSEP=3.7760 based on RVI_((570/800)) and R^(2)=0.7343,RMSECV=3.6535,RE=5.49%,and RMSEP=3.3578 based on NDVI[(570,800)(570,950)(700,840)].The technique to create spatial distribution maps of the relative canopy chlorophyll content in the orchard was established based on sensor information that directly reflected the chlorophyll content of the plants in different parts of the orchard,which in turn provides evidence for implementation of orchard productivity evaluation and precision in fertilization management.

Monoclonal antibody-based serological methods for detecting Citrus tristeza virus in citrus groves

Citrus tristeza virus （CTV） is one of the most economically important citrus viruses and harms the citrus industry worldwide. To develop reliable and effective serological detection assays of CTV, the major capsid protein （CP） gene of CTV was expressed in Escherichia coli BL21 （DE3） using the expression vector pET-28a and purified through Ni＊-NTA affinity chromatography. The recombinant protein was used to immunize BALB/c mice. Four hybridoma cell lines （14B10, 14Hll, 20D5, and 20G12） secreting monoclonal antibodies （MAbs） against CTV were obtained through conventional hybridoma technology. The titers of MAb-containing ascitic fluids secreted by the four hybridoma lines ranged from 10-6 to 10.7 in indirect enzyme-linked immunosorbent assay （ELISA）. Western blots showed that all four MAbs could specifically react with CTV CP. Using the prepared MAbs, dot-ELISA, Tissue print-ELISA, and triple antibody sandwich （TAS）-ELISA were developed to detect CTV in tree nurseries and epidemiological studies. The developed dot-ELISA and TAS-ELISA methods could detect CTV in crude extracts of infected citrus leaves with dilutions of 1：2560 and 1：10, 240 （w/v, g/mL）, respectively. Tissue print-ELISA was particularly useful for large-scale field sample detection, mainly owing to its simplicity and lack of sample preparation requirements. The field survey revealed that CTV is prevalent on citrus trees in the Chongqing Municipality, Jiangxi Province, and Zhejiang Province of China. The coincidence rate of serological and RT-PCR test results reached more than 99.5%. The prepared MAbs against CTV and established sensitive and specific serological assays have a significant role in the detection and prevention and control of CTV in our country.