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Development and Application of Silkworm Disease Recognition System Based on Mobile App

  • Dingyuan XiaEmail author
  • Zhen Yu
  • Anjun Cheng
  • Liang Tang
  • Meining Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11902)

Abstract

Facing the characteristic agriculture of silkworm breeding in China, aiming at the technical requirements of silkworm farmers for accurate recognition and effective prevention and control of silkworm diseases, referring to the “flower companion” App for flower and grass recognition and the Taobao online commodity purchasing system, a silkworm disease image detection and recognition system based on mobile App is developed to fill the gaps in the industry. The system adopts the C/S network architecture mode, and users can collect silkworm disease images in real-time by mobile App and upload them to cloud server platform automatically. The cloud server platform uses efficient image segmentation, feature extraction, SVM-based classification, and feature matching and fast retrieval algorithm, automatically pushes the case analysis report to the mobile phone, displays the case image, and makes brief text descriptions. After online testing by Android mobile phone, the system runs smoothly and has no stagnation. The system response time is less than 0.5 s, and the average retrieval accuracy rate is about 75%. Compared with domestic similar systems, it has unique characteristics and achieves the expected goal of system design, which has certain theoretical significance and application value.

Keywords

Silkworm Disease Recognition Mobile App Android SVM 

Notes

Acknowledgments

This work was supported by the Key Research and Development Project in Guangxi Zhuang Autonomous Region, Nanning, China: the Research and Application of General Survey, Evaluation, Forecast and Prevention & Control Technology of Major Diseases and Pests of Silkworm in Guangxi under Grant No. AB16380102, while a part of this work was supported by the Key Lab. of Broadband Wireless Communications and Sensor Networks, Hubei Province, Wuhan University of Technology, Wuhan, China.

References

  1. 1.
    Dai, J., Lai, J.: Cotton pest and disease diagnosis system based on image rules and Android mobile phone. Trans. Chin. Soc. Agric. Mach. 46(1), 35–43 (2015)Google Scholar
  2. 2.
    Kumar, N., Belhumeur, P.N., Biswas, A., et al.: Leafsnap: a computer vision system for automatic plant species identification. In: 12th European Conference on Computer Vision, pp. 502–516 (2012)Google Scholar
  3. 3.
    Zhao, Z.Q., Ma, L.H., Cheung, Y.M., et al.: Ap leaf: an efficient android-based plant leaf identification system. Neuro-Computing 151, 1112–1119 (2015)Google Scholar
  4. 4.
    Yan, Z., Wang, J.: Image content detection based on android system. Software 33(6), 35–37 (2012)Google Scholar
  5. 5.
    Wu, Y., Luo, T., Wang, M.: Research on image acquisition and retrieval methods of positionable images. Comput. Eng. 40(7), 207–211 (2012)Google Scholar
  6. 6.
    Li, W., Tang, S., Chen, R.: Identification of cucumber leaf disease based on color feature and support vector machine. Agric. Mech. Res. 1, 73–75 (2014)Google Scholar
  7. 7.
    Yang, Q., Gao, X.: Research on identification of main diseases of barley based on color and texture features. J. Chin. Agric. Univ. 5, 129–135 (2013)Google Scholar
  8. 8.
    Carmargo, A., Smith, J.: Image pattern classification for the identification of disease causing agents in plants. Comput. Electron. Agric. 66(2), 121–125 (2009)CrossRefGoogle Scholar
  9. 9.
    Patil, S.P., Zambre, M.R.S.: Classification of cotton leaf spot disease using support vector, machine. Int. J. Eng. Res. Appl. 4(5), 92–97 (2014)Google Scholar
  10. 10.
    Sannakki, S.S., Rajpurohit, V.S., Nargund, V.B.: SVM-DSD: SVM based diagnostic system for the detection of pomegranate leaf diseases. In: Kumar, M.A., Kumar, T. (eds.) Proceedings of International Conference on Advances in Computing, vol. 174, pp. 715–720 (2013).  https://doi.org/10.1007/978-81-322-0740-5_85
  11. 11.
    Chang, T.: Research on wheat disease identification based on support vector machine. Shandong Agricultural University, Taian (2015)Google Scholar
  12. 12.
    Diao, Z.: Research and application of intelligent diagnosis system for wheat leaf disease in Datian. University of Science and Technology of China, Hefei (2010)Google Scholar
  13. 13.
    Wang, A.: Research on classification algorithm of floorboard texture based on mixed Gaussian model. Northeast Forestry University, Harbin (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Dingyuan Xia
    • 1
    Email author
  • Zhen Yu
    • 1
  • Anjun Cheng
    • 1
  • Liang Tang
    • 2
  • Meining Shi
    • 2
  1. 1.School of Information EngineeringWuhan University of TechnologyWuhanChina
  2. 2.Sericulture Technology Promotion Master Station of GuangxiNanningChina

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