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A Study of Image Processing on Identifying Cucumber Disease

  • Yong Wei
  • Ruokui Chang
  • Yuanhong Wang
  • Hua Liu
  • Yanhong Du
  • Jianfeng Xu
  • Ling Yang
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 370)

Abstract

Plant disease has been a major constraining factor in the production of cucumber, the traditional diagnostic methods usually take a long time, and the control period is often missed. We take computer image processing as a method, preprocessing the images of more than 100 sheets of collected samples of cucumber leaves, using the region growing method to extract scab area of leaves to get three feature parameters of shape, color and texture. And then, through the establishment of BP neural network pattern, the model identification accuracy of cucumber leaf disease can reach 80%. The experiment shows that by using this method, the diseases of cucumber leaves can be identified more quickly and accurately. And the feature extraction and automatic diagnosis of cucumber leaf disease can be achieved.

Keywords

cucumber disease texture feature feature extraction 

References

  1. 1.
    Sasaki, Y., Okamoto, T., Imou, K., Torii, T.: Automatic Diagnosis of Plant Disease. Journal of JSAM 61(2), 119–126 (1999)Google Scholar
  2. 2.
    Muhammed, H.H.: Hyperspectralcrop reflectance data for characterizing and estimating fungal disease severity in wheat. Biosystems Engineering 91(1), 9–20 (2005)CrossRefGoogle Scholar
  3. 3.
    Jing, Z., Shuangxi, W., Xiaozhi, D.: A study on method of extract of texture characteristic value in image processing for plant disease of greenhouse. Journal of Shenyang Agricultural University 37(3), 282–285 (2006)Google Scholar
  4. 4.
    Changxing, G., Junxion, Z.: Recognition and Features Extraction of Cucumber Downy Mildew Based on Color and Texture. Transactions of the Chinese Society for Agricultural Machienry 42(3), 170–174 (2011)Google Scholar
  5. 5.
    Bingqi, C., Xuemei, G., Xiaohua, L.: Image diagnosis algorithm of diseased wheat. Transactions of the Chinese Society for Agricultural Machienry 40(12), 190–195 (2009)Google Scholar
  6. 6.
    Hanping, M., Guili, X., Pingping, L.: Diagnosis of nutrientdeficiency of tomato based on computervision. Transactions of the Chinese Society for Agricultural Machienry 34(2), 73–75 (2003)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Yong Wei
    • 1
  • Ruokui Chang
    • 1
  • Yuanhong Wang
    • 2
  • Hua Liu
    • 1
  • Yanhong Du
    • 1
  • Jianfeng Xu
    • 1
  • Ling Yang
    • 3
  1. 1.Department of Electromechanical EngineeringTianjin Agricultural UniversityTianjinP.R. China
  2. 2.Department of HorticultureTianjin Agricultural UniversityTianjinP.R. China
  3. 3.The Center for Agri-Food Quality and SafetyMOABeijingP.R. China

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