• DocumentCode
    3572863
  • Title

    Research on weed recognition method based on invariant moments

  • Author

    Zhao Bo ; Wu Hai Hua ; Li Shu Jun ; Mao Wen Hua ; Zhang Xiao Chao

  • Author_Institution
    State Key Lab. of Soil-Plant-Machine Syst., Chinese Acad. of Agric. Mechanization, Beijing, China
  • fYear
    2014
  • Firstpage
    2167
  • Lastpage
    2169
  • Abstract
    A new method of weed recognition based on the invariant moments was proposed in this paper. Firstly, the area of the soybean leaf was located from the complicated image background. Secondly, the features of soybean leaf were obtained by Hu invariant moments, which are the invariability of the translation, the ratio and the rotation, and have lower computational complexity. Finally, the soybean leaf was recognized by the nearest neighbor classifier, and other image information were identified to weed. Experimental results proved that the weed recognition method was effective on the different environment, and could location the weed rapidly, reliably and accurately. The correct rate of the weed recognition was 90.5% in the ordinary environment, the average cost time was 670ms.
  • Keywords
    computational complexity; crops; image classification; Hu invariant moments; average cost time; complicated image background; computational complexity; image information identification; nearest neighbor classifier; soybean leaf area location; soybean leaf features; weed recognition method; Artificial intelligence; Character recognition; Feature extraction; Image color analysis; Image recognition; Noise; Shape; Image preprocessing; Invariant moments; Weed recognition; soybean leaf;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation (WCICA), 2014 11th World Congress on
  • Type

    conf

  • DOI
    10.1109/WCICA.2014.7053057
  • Filename
    7053057