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Development of a Recognition System for Alfalfa Leaf Diseases Based on Image Processing Technology

  • Feng Qin
  • Haiguang Wang
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 509)

Abstract

To implement rapid identification and diagnosis of leaf diseases on alfalfa, an image-based recognition system was developed using the GUIDE platform under MATLAB software environment. An integrated segmentation method of K_median clustering algorithm and linear discriminant analysis was applied to implement the lesion image segmentation in this developed recognition system. A multinomial logistic regression model for disease recognition was built based on 21 color, shape and texture features selected by using correlation-based feature selection method. Using this system, disease image reading, image segmentation and lesion image recognition can be done. This system can be applied to conduct image recognition of four common kinds of leaf diseases on alfalfa including alfalfa Cercospora leaf spot, alfalfa rust, alfalfa common leaf spot and alfalfa Leptosphaerulina leaf spot. Some basis was provided for further development of image recognition system of various alfalfa diseases and for building a network-based automatic diagnosis system of alfalfa diseases.

Keywords

Alfalfa leaf disease Image processing Image recognition GUIDE platform Multinomial logistic regression analysis 

Notes

Acknowledgments

This work was supported by Special Fund for Agro-scientific Research in the Public Interest of China (201303057).

References

  1. 1.
    He, F., Han, D.M., Wan, L.Q., Li, X.L.: The nutrient situations in the major alfalfa producing areas of China. J. Plant Nutri. Fert. 20, 503–509 (2014). (in Chinese)Google Scholar
  2. 2.
    Liu, A.P., Hou, T.J.: Pests and Their Control of Grassland Plants. China Agricultural Science and Technology Press, Beijing (2005). (in Chinese)Google Scholar
  3. 3.
    Samac, D.A., Rhodes, L.H., Lamp, W.O.: Compendium of Alfalfa Diseases and Pests, 3rd edn. APS Press, St. Paul (2014)Google Scholar
  4. 4.
    Li, Y.Z., Nan, Z.B.: The Methods of Diagnose, Investigation and Loss Evaluation for Forage Diseases. Phoenix Science Press, Nanjing (2015). (in Chinese)Google Scholar
  5. 5.
    Pydipati, R., Burks, T.F., Lee, W.S.: Identification of citrus disease using color texture features and discriminant analysis. Comput. Electron. Agric. 52, 49–59 (2006)CrossRefGoogle Scholar
  6. 6.
    Sankaran, S., Mishra, A., Ehsani, R., Davis, C.: A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 72, 1–13 (2010)CrossRefGoogle Scholar
  7. 7.
    Patil, J.K., Kumar, R.: Advances in image processing for detection of plant diseases. J. Adv. Bioinform. Res. 2, 135–141 (2011)Google Scholar
  8. 8.
    Li, G.L., Ma, Z.H., Wang, H.G.: Image recognition of wheat stripe rust and wheat leaf rust based on support vector machine. J. China Agric. Univ. 17, 72–79 (2012). (in Chinese)Google Scholar
  9. 9.
    Li, G.L., Ma, Z.H., Wang, H.G.: Image recognition of grape downy mildew and grape powdery mildew based on support vector machine. In: Li, D., Chen, Y. (eds.) CCTA 2011, part III, IFIP AICT, vol. 370, pp. 151–162. Springer, New York (2012).  https://doi.org/10.1007/978-3-642-27275-2_17CrossRefGoogle Scholar
  10. 10.
    Xie, C.Y., Wu, D.K., Wang, C.Y., Li, Y.: Insect pest leaf detection system based on information fusion of image and spectrum. Trans. Chin. Soc. Agric. Mach. 44, 269–272 (2013). (in Chinese)Google Scholar
  11. 11.
    Phadikar, S., Sil, J., Das, A.K.: Rice diseases classification using feature selection and rule generation techniques. Comput. Electron. Agric. 90, 76–85 (2013)CrossRefGoogle Scholar
  12. 12.
    Barbedo, J.G.A.: Digital image processing techniques for detecting. Quantifying Classifying Plant Dis. 2, 660 (2013)Google Scholar
  13. 13.
    Barbedo, J.G.A.: An automatic method to detect and measure leaf disease symptoms using digital image processing. Plant Dis. 98, 1709–1716 (2014)CrossRefGoogle Scholar
  14. 14.
    Zhou, R., Kaneko, S., Tanaka, F., Kayamori, M., Shimizu, M.: Image-based field monitoring of Cercospora leaf spot in sugar beet by robust template matching and pattern recognition. Comput. Electron. Agric. 116, 65–79 (2015)CrossRefGoogle Scholar
  15. 15.
    Tan, W.X., Zhao, C.J., Wu, H.R., Gao, R.H.: A deep learning network for recognizing fruit pathologic images based on flexible momentum. Trans. Chin. Soc. Agric. Mach. 46, 20–25 (2015). (in Chinese)Google Scholar
  16. 16.
    Mutka, A.M., Bart, R.S.: Image-based phenotyping of plant disease symptoms. Front. Plant Sci. 5, 734 (2015)CrossRefGoogle Scholar
  17. 17.
    Barbedo, J.G.A.: A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst. Eng. 144, 52–60 (2016)CrossRefGoogle Scholar
  18. 18.
    Ye, H.J., Lang, R., Liu, C.Q., Li, M.Z.: Recognition of cucumber downy mildew disease based on visual saliency map. Trans. Chin. Soc. Agric. Mach. 47, 270–274 (2016). (in Chinese)Google Scholar
  19. 19.
    Mengistu, A.D., Alemayehu, D.M., Mengistu, S.G.: Ethiopian coffee plant diseases recognition based on imaging and machine learning techniques. Int. J. Database Theor. Appl. 9, 79–88 (2016)CrossRefGoogle Scholar
  20. 20.
    Zheng, J., Liu, L.B.: Design and application of rice disease image recognition system based on Android. Comput. Eng. Sci. 37, 1366–1371 (2015). (in Chinese)Google Scholar
  21. 21.
    Li, G.L., Ma, Z.H., Wang, H.G.: Development of a single-leaf disease severity automatic grading system based on image processing. In: Lu, W., Cai, G., Liu, W., Xing, W. (eds.) Proceedings of the 2012 International Conference on Information Technology and Software Engineering. LNEE, vol. 212, pp. 665–675. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-34531-9_70Google Scholar
  22. 22.
    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 Recogn. 53, 287–299 (2016)CrossRefGoogle Scholar
  23. 23.
    Le Cessie, S., Van Houwelingen, J.C.: Ridge estimators in logistic regression. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 41, 191–201 (1992)zbMATHGoogle Scholar
  24. 24.
    Otsu, N.A.: Threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)CrossRefGoogle Scholar
  25. 25.
    Qin, F., Liu, D.X., Sun, B.D., Ruan, L., Ma, Z.H., Wang, H.G.: Recognition of four different alfalfa leaf diseases based on image processing technology. J. China Agric. Univ. 21, 65–75 (2016). (in Chinese)Google Scholar
  26. 26.
    Powers, D.M.W.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Tech. 2, 37–63 (2011)Google Scholar
  27. 27.
    Stricker, M.A., Orengo, M.: Similarity of color images. In: Proceedings of SPIE International Society Optics and Engineering, vol. 2420, pp. 381–392 (1995)Google Scholar
  28. 28.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB. Publishing House of Electronics Industry, Beijing (2005). (in Chinese)Google Scholar
  29. 29.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Publishing House of Electronics Industry, Beijing (2011). (in Chinese)Google Scholar
  30. 30.
    Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. thesis, The University of Waikato, Hamilton, New Zealand (1999)Google Scholar
  31. 31.
    Qin, F., Liu, D.X., Sun, B.D., Ruan, L., Ma, Z.H., Wang, H.G.: Identification of alfalfa leaf diseases using image recognition technology. PLoS ONE 11, e0168274 (2016).  https://doi.org/10.1371/journal.pone.0168274CrossRefGoogle Scholar
  32. 32.
    Qin, F., Liu, D.X., Sun, B.D., Ruan, L., Ma, Z.H., Wang, H.G.: Image recognition of four different alfalfa leaf diseases based on deep learning and support vector machine. J. China Agric. Univ. 22, 123–133 (2017). (in Chinese)Google Scholar
  33. 33.
    Liu, L.Q., Yuan, Z.B., Deng, J.Z., Li, M., Jin, J.: Construction of Tilletia diseases diagnosis system. Plant Quarantine 28, 10–15 (2014). (in Chinese)Google Scholar
  34. 34.
    Aji, A.F., Munajat, Q., Pratama, A.P., Kalamullah, H., Setiyawan, J., Arymurthy, A.M.: Detection of palm oil leaf disease with image processing and neural network classification on mobile device. Int. J. Comput. Theor. Eng. 5, 528–532 (2013)CrossRefGoogle Scholar
  35. 35.
    Xia, Y.Q., Wang, H.M., Zeng, S.: Plant leaf image disease detection based on Android. J. Zhengzhou Univ. Light Ind. (Nat. Sci. Ed.) 29, 71–74 (2014). (in Chinese)Google Scholar
  36. 36.
    Qu, Y., Tao, B., Wang, Z.J., Wang, S.T.: Design of apple leaf disease recognition system based on Android. J. Agric. Univ. Hebei 38, 102–106 (2015). (in Chinese)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.Department of Plant PathologyChina Agricultural UniversityBeijingChina

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