Classification of Water Stress in Sunagoke Moss Using Color Texture and Neural Networks

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著者

    • Murase Haruhiko MURASE Haruhiko
    • Bioinstrumentation, Control and Systems (BICS) Engineering Laboratory, School of Life and Environmental Sciences, Osaka Prefecture University

抄録

The general appearance of a plant is the most obvious indicator of its physiological well- being. Color Co-occurrence Matrix (CCM) texture features, extracted from a set of 1095 images were used to classify water stress in Sunagoke moss (<I>Rhacomitrium canescens</I>) using Neural Networks (NN). An Excess Green Water Stress Index (EGWSI) was developed and used to quantify water stress in the sample. The CCM texture features were extracted from: red-green-blue (RGB); hue-saturation-intensity (HSI) and CIE's (Comission Internationale de LEclairage) LAB and XYZ color spaces. The HSI texture features achieved 99.45% water stress classification efficiency. They were followed by RGB, XYZ and LAB texture features with classification efficiencies of 99.07%, 98.83 and 96.3% in that order respectfully. The HSI textures features displayed a higher ability and reliability to classify water stress in Sunagoke moss and can be used for stress detection under varying light intensities. A significant accomplishment of this study was the detection of both flood and draught water stress in a plant that exhibits a high level of desiccation tolerance. This provides an opportunity for the possibility of allowing plants to control their own bioproduction environments.

収録刊行物

  • Environment control in biology  

    Environment control in biology 46(1), 21-29, 2008-03-30 

    Japanese Society of Agricultural, Biological and Environmental Engineers and Scientists

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各種コード

  • NII論文ID(NAID)
    10021229395
  • NII書誌ID(NCID)
    AA12029220
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    1880554X
  • NDL 記事登録ID
    9478961
  • NDL 雑誌分類
    ZR1(科学技術--生物学)
  • NDL 請求記号
    Z78-A361
  • データ提供元
    CJP書誌  CJP引用  NDL  J-STAGE 
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