• DocumentCode
    2615338
  • Title

    A Research of Maize Disease Image Recognition of Corn Based on BP Networks

  • Author

    Kai, Song ; Zhikun, Liu ; Hang, Su ; Chunhong, Guo

  • Author_Institution
    Info. Sci. & Eng. Coll., ShenYang Ligong Univ., Shenyang, China
  • Volume
    1
  • fYear
    2011
  • fDate
    6-7 Jan. 2011
  • Firstpage
    246
  • Lastpage
    249
  • Abstract
    A Research of maize disease image recognition of corn leaf based on image processing and analysis, which is to study diseases of image classification. According to the texture characteristics of corn diseases, it uses YCbCr color space technology to segment disease spot, and uses the cooccurrence matrix spatial gray level layer to extract disease spot texture feature, and uses BP neural network to class the maize disease. Application YCbCr color space technology segmented disease spot, and using the co-occurrence matrix spatial gray level layer extracted disease spot texture feature of using BP neural network, on maize disease classification identification. On VC++ platform, do experiments for the study design recognition algorithm, the experimental results show that the algorithm can effectively identify the disease image, the accuracy was as high as 98% or more, the study provided the theoretical basis to cognition of maize leaf disease. the image re of maize leaf disease image recognition to provide a theoretical basis.
  • Keywords
    backpropagation; crops; diseases; image classification; image colour analysis; image segmentation; neural nets; BP neural network; VC++ platform; YCbCr color space technology; cooccurrence matrix spatial gray level layer; corn leaf; image classification; maize disease image recognition; Diseases; Feature extraction; Image color analysis; Image segmentation; Lesions; Pixel; Presses;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Measuring Technology and Mechatronics Automation (ICMTMA), 2011 Third International Conference on
  • Conference_Location
    Shangshai
  • Print_ISBN
    978-1-4244-9010-3
  • Type

    conf

  • DOI
    10.1109/ICMTMA.2011.66
  • Filename
    5720767