Detection of citrus Huanglongbing based on image feature extraction and two-stage BPNN modeling
Keywords:
citrus leaf, Huanglongbing, texture and color features, feature extraction, two-stage back propagation neural networkAbstract
Abstract: Citrus Huanglongbing (HLB), which is spread by the citrus psyllid, is the most destructive disease of citrus industry. While no effective cure for the disease has been reported, detection and removal of infected trees can prevent spreading. Symptoms indicative of HLB can be present in both HLB-positive trees and HLB-negative trees, making identification of infected trees difficult. A detection method for citrus HLB based on image feature extraction and two-stage back propagation neural network (BPNN) modeling was investigated in this research. The identification method for eight different classes including healthy, HLB and non-HLB symptoms was studied. Thirty-four statistical features including color and texture were extracted for each leaf sample, following the two-stage BPNN to model and identify HLB-positive leaves from HLB-negative leaves. The discrimination accuracy can reach approximately 92% which shows that this method based on visual image processing can perform well in detecting citrus HLB. Keywords: citrus leaf, Huanglongbing, texture and color features, feature extraction, two-stage back propagation neural network DOI: 10.3965/j.ijabe.20160906.1895 Citation: Deng X L, Lan Y B, Xing X Q, Mei H L, Liu J K, Hong T S. Citrus Huanglongbing detection based on image feature extraction and two-stage back propagation neural network modeling. Int J Agric & Biol Eng, 2016; 9(6): 20-26.References
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[2] Fan G, Liu B, Wu R, Li T, Cai Z, Ke C. Thirty years of research on citrus Huanglongbing in China. Fujian Journal of Agricultural Sciences, 2009; 24(2): 183–190. (in Chinese with English abstract)
[3] Durborow S. An analysis of the potential economic impact of Huanglongbing on the California citrus industry. Southern Agricultural Economics Association Annual Meeting, Orlando, FL, 2013-2-3.
[4] Gao Y, Lu Z, Liu Z, Zhong B. Research progress on diagnostic methods of citrus Huanglongbing. Journal of Gannan Normal University, 2013; 3: 37– 40. (in Chinese
with English abstract)
[5] Chen Z, Li D. Preliminary studies on methods for rapid diagnosis of citrus yellow shoot disease (CYS). Journal of Zhejiang Agricultural University, 1987; 13(4): 348–354. (in Chinese)
[6] Zhang W. Study on PCR detection of citrus Huanglongbing bacterium. Biological Disaster Science, 2012; 35(2): 164–168.
[7] Pereira F, Milori D, Pereira-Filho E, Venâncio A, Russo M , Cardinali M, et al. Laser-induced fluorescence imaging method to monitor citrus greening disease. Computers and Electronics in Agriculture, 2011; 79(1): 90–93.
[8] Deng X, Li Z, Deng X, Hong T. Citrus disease recognition based on weighted scalable vocabulary tree. Precision Agriculture, 2014; 15(3): 321–330.
[9] Kim D G, Burks T F, Schumann A W, Zekri M, Zhao X H, Qin J. Detection of citrus greening using microscopic imaging. Agricultural Engineering International: the CIGR Journal, 2009; Manuscript 1194. Vol. XI. June.
[10] Pourreza A, Lee W S, Raveh E, Ehsani R, Etxeberria E. Citrus Huanglongbing detection using narrow-band imaging and polarized illumination. Transactions of the ASABE, 2014; 57(1): 259–272.
[11] Mei H, Deng X, Hong T, Luo X, Deng X. Early detection and grading of citrus Huanglongbing using hyperspectral imaging technique. Transactions of the CSAE, 2014; 30(9): 140–147. (in Chinese with English abstract)
[12] Deng X, Zheng J, Mei H, Li Z, Deng X, Hong T. Identification and classification of citrus Huanglongbing disease based on hyperspectral imaging. Journal of Northwest A&F University (Nat. Sci. Ed.), 2013; 41(7): 99–105. (in Chinese with English abstract)
[13] Deng X, Kong C, Wu W, Mei H, Li Z, Deng X, et al. Detection of citrus Huanglongbing based on principal component analysis and back propagation neural network. Acta Photonica Sinica, 2014; 43(4): 1–7. (in Chinese with English abstract)
[14] Li X, Lee W S, Li M, Ehsani R, Mishra A R, Yang C, et al. Comparison of different detection methods for citrus greening disease based on airborne multispectral and hyperspectral imagery. ASABE Paper. 2011; No.1110570. St. Joseph, Mich.: ASABE.
[15] Li X, Lee W S, Li M, Ehsani R, Mishra A R, Yang C, et al. Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Computers and Electronics in Agriculture, 2012; 83(4): 32–46.
[16] Li H, Lee W S, Wang K, Ehsani R, Yang C. Extended spectral angle mapping (ESAM) for citrus greening disease detection using airborne hyperspectral imaging. Precision Agric, 2014; 15(2): 162–183.
[17] Li H, Lee W S, Wang R, Ehsani R, Yang C. Spectral angle mapper (SAM) based citrus greening disease detection using airborne hyperspectral imaging. Proc. 11th Intl Conf. on Precision Agriculture. Monticello, Ill.: International Society of Precision Agriculture, 2012.
[18] Lu Y, Guan H, Zhao B, Yang L. Study on the method of image pre-processing and feature extraction for rice diseases. Journal of Agricultural Mechanization Research, 2011; 33(8): 27–30. (in Chinese with English abstract)
[19] Wu S. Based on sobel edge detection operator of matlab implementation. Computer Knowledge and Technology, 2010; 16(19): 5314–5315.
[20] Guo D, Song Z. A Study on texture image classifying based on gray-level co-occurrence matrix. Forestry Machinery & Woodworking Equipment, 2005; 33(7): 21–23. (in Chinese with English abstract)
[21] Zhang J, Wang S, Dong X, Cheng P. A Study on Method of Extract of Texture Characteristic Value in Image Processing for Plant Disease of Greenhouse. Journal of Shenyang Agricultural University, 2006; 37(3): 282–285. (in Chinese with English abstract)
[22] Yang J, Liu C. Study of color space and its conversions in digital image processing. Journal of Shangqiu Vocational and Technical College, 2009; 8(2): 25–31. (in Chinese with English abstract)
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Published
2016-12-01
How to Cite
Xiaoling, D., Lan, Y., Xiaqiong, X., Huilan, M., Jiakai, L., & Tiansheng, H. (2016). Detection of citrus Huanglongbing based on image feature extraction and two-stage BPNN modeling. International Journal of Agricultural and Biological Engineering, 9(6), 20–26. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/1895
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Applied Science, Engineering and Technology
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