Development of automatic counting system for urediospores of wheat stripe rust based on image processing

Li Xiaolong, Ma Zhanhong, Fernando Bienvenido, Qin Feng, Wang Haiguang, José Antonio Álvarez-Bermejo

Abstract


To realize automatic counting of urediospores of Puccinia striiformis f. sp. tritici (Pst) (causal agent of wheat stripe rust), an automatic counting system for urediospores of wheat stripe rust pathogen based on image processing was developed using MATLAB GUIDE platform in combination with Local C Compiler (LCC). The system is independent of the MATLAB environment and can be run on a computer without the MATLAB software. Using this system, automatic counting of Pst urediospores in a microscopic image can be implemented via image processing technologies including image scaling, clustering segmentation, morphological modification, watershed transformation, connected region labeling, etc. Structure design of the automatic counting system, the key algorithms used in the system and realization of the main functions of the system were described in detail. Spore counting tests were conducted using microscopic digital images of Pst urediospores and the high accuracies more than 95% were obtained. The results indicated that it is feasible to count Pst urediospores automatically using the developed system based on image processing.
Keywords: puccinia striiformis f. sp. tritici, wheat stripe rust, image processing, automatic counting, computer aided system, MATLAB
DOI: 10.25165/j.ijabe.20171005.3084

Citation: Li X L, Ma Z H, Bienvenido F, Qin F, Wang H G, Alvarez-Bermejo J A. Development of automatic counting system for urediospores of wheat stripe rust based on image processing. Int J Agric & Biol Eng, 2017; 10(5): 134–143.

Keywords


Puccinia striiformis f. sp. tritici, wheat stripe rust, image processing, automatic counting, computer aided system, MATLABpuccinia striiformis f. sp. tritici, wheat stripe rust, image processing, automatic counting, computer aided system, MATLAB

References


Li Z Q, Zeng S M. Wheat rusts in China. Beijing: China Agriculture Press, 2002; pp. 1–254. (in Chinese).

Chen W Q, Kang Z S, Ma Z H, Xu S C, Jin S L, Jiang Y Y. Integrated management of wheat stripe rust caused by Puccinia striiformis f. sp. tritici in China. Scientia Agricultura Sinica, 2013; 46(20): 4254–4262. (in Chinese)

Wang X J, Ma Z H, Jiang Y Y, Shi S D, Liu W C, Zeng J, et al. Modeling of the overwintering distribution of Puccinia striiformis f. sp. tritici based on meteorological data from 2001 to 2012 in China. Frontiers of Agricultural Science and Engineering, 2014; 1(3): 223–235.

Campbell C L, Madden L V. Introduction to plant disease epidemiology. New York: John Willey and Sons, 1990; pp. 75–105.

Xiao Y Y, Ji B H, Yang Z W, Jiang R Z. Epidemic and forecast of plant diseases (Second Edition). Beijing: China Agricultural University Press, 2005; pp.87–89. (in Chinese)

Calderon C, Ward E, Freeman J, Foster S J, McCartney H A. Detection of airborne inoculum of Leptosphaeria maculans and Pyrenopeziza brassicae in oilseed rape crops by polymerase chain reaction (PCR) assays. Plant Pathology, 2002; 51(3): 303–310.

Luo Y, Ma Z, Reyes H C, Morgan D, Michailides T J. Quantification of airborne spores of Monilinia fructicola in stone fruit orchards of California using real-time PCR. European Journal of Plant Pathology, 2007; 118(2): 145–154.

Cao X R, Yao D M, Zhou Y L, West J S, Xu X M, Luo Y, et al. Detection and quantification of airborne inoculum of Blumeria graminis f. sp. tritici using quantitative PCR. European Journal of Plant Pathology, 2016; 146(1): 225–229.

Wang H G, Ma Z H, Zhang M R, Shi S D. Application of computer technology in plant pathology. Agriculture Network Information, 2004; 19(10): 31–34. (in Chinese)

Pydipati R, Burks T F, Lee W S. Statistical and neural network classifiers for citrus disease detection using machine vision. Transactions of the ASAE, 2005; 48(5): 2007–2014.

Camargo A, Smith J S. An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems Engineering, 2009; 102(1): 9–21.

Zhang C L, Zhang S W, Yang J C, Shi Y C, Chen J. Apple leaf disease identification using genetic algorithm and correlation based feature selection method. Int J Agric & Biol Eng, 2017; 10(2): 74–83.

Sankaran S, Mishra A, Ehsani R, Davis C. A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture, 2010; 72(1): 1–13.

Li G L, Ma Z H, Huang C, Chi Y W, Wang H G. Segmentation of color images of grape diseases using K_means clustering algorithm. Transactions of the CSAE, 2010; 26(Supp.2): 32–37. (in Chinese)

Patil J K, Kumar R. Advances in image processing for detection of plant diseases. Journal of Advanced Bioinformatics Applications and Research, 2011; 2(2): 135–141.

Leiva-Valenzuela G A, Aguilera J M. Automatic detection of orientation and diseases in blueberries using image analysis to improve their postharvest storage quality. Food Control, 2013; 33(1): 166–173.

Phadikar S, Sil J, Das A K. Rice diseases classification using feature selection and rule generation techniques. Computers and Electronics in Agriculture, 2013; 90: 76–85.

Omrani E, Khoshnevisan B, Shamshirband S, Saboohi H, Anuar N B, Nasir M H N M. Potential of radial basis function-based support vector regression for apple disease detection. Measurement, 2014; 55: 512–519.

Barbedo J G A. An automatic method to detect and measure leaf disease symptoms using digital image processing. Plant Disease, 2014; 98(12): 1709–1716.

Qin F, Liu D, Sun B, Ruan L, Ma Z, Wang H. Identification of alfalfa leaf diseases using image recognition technology. PLoS ONE, 2016; 11(12): e0168274. doi: 10.1371/journal.pone.0168274.

Martin D P, Rybicki E P. Microcomputer-based

quantification of maize streak virus symptoms in Zea mays. Phytopathology, 1998; 88(5): 422–427.

Bock C H, Parker P E, Cook A Z, Gottwald T R. Visual rating and the use of image analysis for assessing different symptoms of citrus canker of grapefruit leaves. Plant Disease, 2008; 92(4): 530–541.

Li G L, Ma Z H, Wang H G. An automatic grading method of severity of single leaf infected with grape downy mildew based on image processing. Journal of China Agricultural University, 2011; 16(6): 88–93. (in Chinese)

Contreras-Medina L M, Osornio-Rios R A, Torres-Pacheco I, Romero-Troncoso R D, Guevara-Gonzalez R G, Millan-Almaraz J R. Smart sensor for real-time quantification of common symptoms present in unhealthy plants. Sensors, 2012; 12(1): 784–805.

Atoum Y, Afridi M J, Liu X M, McGrath J M, Hanson L E. On developing and enhancing plant-level disease rating systems in real fields. Pattern Recognition, 2016; 53: 287–299.

Yue L L, Yang M, Peng L. A study of spore image classification based on feature extraction. Applied Mechanics and Materials, 2014; 556-562: 4774–4778.

Xu P Y, Li J G. Computer assistance image processing spores counting. Proceedings of 2009 International Asia Conference on Informatics in Control, Automation, and Robotics (CAR 2009), 2009, pp.203–206.

Li X L, Ma Z H, Sun Z Y, Wang H G. Automatic counting for trapped urediospores of Puccinia striiformis f. sp. tritici based on image processing. Transactions of the CSAE, 2013; 29(2): 199–206. (in Chinese)

Qi L, Jiang Y, Li Z H, Ma X, Zheng Z X, Wang W J. Automatic detection and counting method for spores of rice blast based on micro image processing. Transactions of the CSAE, 2015; 31(12): 186–193. (in Chinese)

Tao M C, Zhao J P, Zhang Y K, Wang C, He L L, Zhou H. Design of field spore capture and automatic counting. Journal of Inner Mongolia Agricultural University (Natural Science Edition), 2016; 37(1): 105–109. (in Chinese)

Yao Q, Xian D X, Liu Q J, Yang B J, Diao G Q, Tang J. Automated counting of rice planthoppers in paddy fields based on image processing. Journal of Integrative Agriculture, 2014; 13(8): 1736–1745.

Wu Z Q, Wang P, Ding T H. Application of active contour model in overlapped algae cells counting. Computer Engineering, 2012; 38(3): 209–211. (in Chinese)

Zhang J X, Xiao Q, Fang C X, Fan C P, Zhu S Q, Zhang L Z. Plaque electronic image segment and automatic counting by computer. Virologica Siniga, 2003; 18(4): 387–390. (in Chinese)

Selim S Z, Ismail M A. K-means-type algorithm: A generalized convergence theorem and characterization of local optimality. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984; 6(1): 81–87.

Derniame J C, Kaba B A, Wastell D G. Software process: Principles, methodology and technology. Lecture Notes in Computer Science 1500, London: Springer-Verlag, 1999. pp. 1–320.

Sonka M, Hlavac V, Boyle R. Image processing, analysis, and machine vision (Third Edition). Toronto: Thomson Engineering, 2007; pp.1–850.


Full Text: PDF

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.