Estimation of Evapotranspiration Rate Using Neural Network with Plant Motion

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Two neural network (NN) models were developed to estimate evapotranspiration (ET) rate of New Guinea Impatiens (<I>Impatiens New Guinea Hibrid</I>). Inputs of one NN model were canopy temperature, environmental factors (air temperature, relative humidity, radiation), and the plant motion (optional). The plant motion was calculated using the top projected canopy area. The mechanistic model was used in order to provide a baseline with which to compare performances of the NN models. In non-drought stress condition, root mean square error (RMSE) between estimated and measured ET rate of the NN model with the plant motion (NNP), the NN model without plant motion (NN), and the mechanistic model were 21.80%, 22.04%, and 29.94%, respectively. In drought stress condition, RMSE of the NNP, the NN, and the mechanistic model were 39.02%, 49.81%, and 72.09%, respectively. The plant motion could contribute the better performance when the plants were in drought stress condition. The NN model could estimate the ET rate without parameters used in the mechanistic model.

収録刊行物

  • Environment control in biology

    Environment control in biology 46(1), 13-19, 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)
    10021229382
  • 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
    9478957
  • 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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