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

Abstract

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 HLB-infected leaves without symptoms is possible, which provides a basis for the hyperspectral diagnosis of citrus with HLB.

Keywords

citrus / HLB / hyperspectral / identification / PCR

References

[1] Yuan Y W, Ge L Q, Wang D P, Lin C Y. Influence of cruise HLB on the yield and quality. Journal of Zhejiang Agricultural Sciences, 2007, (1): 87–88 (in Chinese)

[2] An H, Wang H G, Liu R Y, Cai C J, Ma Z H. Preliminary study on spectral characteristics of single leaf infected by Puccinia striiformis. China Plant Protection, 2006, 25(11): 8–11 (in Chinese)

[3] Qiao H B, Xia B, Ma X M, Cheng D F, Zhou Y L. Identification of damage by diseases and insect pests in winter wheat. Journal of Triticeae Crops, 2010, 30(4): 770–774 (in Chinese)

[4] Bo L, Liu Z Y, Huang J F, Zhang L L, Zhou W, Shi J J. Hyperspectral identification of rice diseases and pests based on principal component analysis and probabilistic neural network. Transactions of the Chinese Society of Agricultural Engineering, 2009, 25(9): 143–147 (in Chinese)

[5] Di W, Cao F, Zhang H, Sun G M, Feng L, He Y. Study on disease level classification of rice panicle blast based on visible and near infrared spectroscopy. Spectroscopy and Spectral Analysis, 2009, 29(12): 3295–3299 (in Chinese)

[6] Li J P, Chai A L, Shi Y X, Xie X W, Li B J. Early rapid detection of clubroot of Chinese cabbage using FTIR spectroscopy. Spectroscopy and Spectral Analysis, 2013, 33(6): 1528–1531 (in Chinese)

[7] Wu D, Zhu D S, He Y, Zhang C Q, Feng L. Nondestructive detection of grey mold of eggplant based on ground multi-spectral imaging sensor. Spectroscopy and Spectral Analysis 2008, 28(7): 1496–1500 (in Chinese)

[8] Feng L, Zhang D R, Chen S S, Feng B, Xie C Q, Chen Y Y. Early detection of gray mold on eggplant leaves using hyperspectral imaging technique. Journal of Zhejiang University, 2012, 38(3): 311–317 (in Chinese)

[9] Tian Y W, Li T L, Zhang L, Wang X J. Diagnosis method of cucumber disease with hyperspectral imaging in greenhouse. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(5): 202–206 (in Chinese)

[10] Feng L, Chen S S, Feng B, Liu F, He Y, Lou B G. Early detection of soybean pod anthracnose based on spectrum technology. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(1): 139–144 (in Chinese)

[11] Feng L, Chen S S, Feng B, Liu F, He Y, Lou B G. Spectral detection on disease severity of soybean pod anthracnose. Transactions of the Chinese Society of Agricultural Machinery, 2012, 43(8): 175–179 (in Chinese)

[12] Chen B, Wang K R, Li S K, Jin X L, Chen J L, Zhang D S. The effects of disease stress on spectra reflectance and chlorophyll fluorescence characteristics of cotton leaves. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(9): 86–93 (in Chinese)

[13] Chen B, Wang K R, Li S K, Jin X L, Chen J L, Zhang D S. Estimating severity level of cotton disease based on spectral indices of TM image. Journal of Infrared and Millimeter Waves, 2011, 30(5): 451–457 (in Chinese)

[14] Sankaran S, Mishra A, Maja J M, Ehsani R. Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards. Computers and Electronics in Agriculture, 2011, 77(2): 127–134

[15] Mishra A, Ehsani R, Karimi D, Albrigo L G. Potential applications of multiband spectroscopy and hyperspectral imaging for detecting HLB infected orange trees. Proceedings of the Annual Meeting of the Florida State Horticultural Society, 2009, 122: 147–151

[16] Kumar A, Lee W S, Ehsani R J, Albrigo L G, Yang C, Mangan R L. Citrus greening disease detection using aerial hyperspectral and multispectral imaging techniques. Journal of Applied Remote Sensing, 2012, 6(1): 063542

[17] Mei H L, Deng X L, Hong T S, Luo X, Deng X L. Early detection and grading of citrus Huanglongbing using hyperspectral imaging technique. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(9): 140–147 (in Chinese)

[18] Deng X L, Kong C, Wu W B, Mei H L, Li Z. Detection of citrus Huanglongbing based on principal component analysis and back propagation neural network.   Acta Photonica Sinica, 2014, 43(4): 16–22 (in Chinese)

[19] Deng X L, Zheng J B, Mei H L. Identification and classification of citrus Huanglongbing disease based on hyperspectral imaging.   Journal of Northwest A&F University (Natural Science Edition), 2013, 41(7): 99–105 (in Chinese)

[20] Tian X, He S L, Lü Q, Yi S L, Xie R J, Zheng Y Q, Liao Q H, Deng L. Determination of photosynthetic pigments in citrus leaves based on hyperspectral images datas. Spectroscopy and Spectral Analysis, 2014, 34(9): 2506–2512 (in Chinese)

[21] Gacher E, Martin G G. Detection of genetically modified organism (GMO) by PCR: a brief review of methodologies available. Trends in Food Science & Technology, 1999, 9(11–12): 380–388

[22] Jagoueix S, Bove J M, Garnier M. The phloem-limited bacterium of greening disease of citrus is a member of the alpha subdivision of the Proteobacteria. Tnternational Journal of Systematic and Evolutionary Microbiology, 1994, 44(3): 379–386

[23] Hu H, Yin Y, Zhang L. Detection of citrus Huanglongbing by conventional and two fluorescence quantitative PCR assays. Scientia Agricultura Sinica, 2006, 39(12): 2491–2497 (in Chinese)

[24] Yan W W, Zhu H D, Shao H H. Soft sensor modeling based on support vector machines. Acta Simulata Systematica Sinica, 2003, 15(10): 1494–1496 (in Chinese)

Acknowledgements

This work was supported by the 2011 Collaborative Innovation Center of the Southern Mountain Orchard Intelligent Management Technology and Equipment of Jiangxi Province (Jiangxi Finance Instruction No. 156 [2014]); the National Key R&D Program of China (2016YFD0200703).

Compliance with ethics guidelinesƒKejian Wang, Dongmei Guo, Yao Zhang, Lie Deng, Rangjin Xie, Qiang Lv, Shilai Yi, Yongqiang Zheng, Yanyan Ma, and Shaolan He declare that they have no conflicts of interest or financial conflicts to disclose.ƒThis article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

The Author(s) 2019. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)