Non-destructive Growth Measurement of Cabbage Plug Seedlings Population by Image Information. (Part 2). Growth Measurement by Neural Network Model.

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  • SUZUKI Toshiyuki
    The Japanese Society of Agricultural Machinery Osaka Prefectural Agricultural & Forestry Research Center
  • MURASE Haruhiko
    The Japanese Society of Agricultural Machinery Factory of Agriculture, Univercity of Osaka Prefecture
  • HGNAMI Nobuo
    The Japanese Society of Agricultural Machinery Factory of Agriculture, Univercity of Osaka Prefecture

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Other Title
  • 画像情報によるキャベツセル成型苗個体群の非破壊生育計測　（第２報）　　ニューラルネットワークモデルによる地上部生育計測
  • ガゾウ ジョウホウ ニ ヨル キャベツセル セイケイ ナエ コタイグン ノ ヒハカイ セイイク ケイソク ダイ2ホウ ニューラル ネットワーク モデル ニ ヨル チジョウブ イクセイ ケイソク
  • Growth Measurement by Neural Network Model
  • ニューラルネットワークモデルによる地上部生育計測

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Abstract

The objective of this study is a non-destructive growth measurement of the plug seedlings population using their image information. In this report, a neural network model for the non-destructive measurement of the leaf area and top fresh weight of the cabbage plug seedlings population was developed. The inputs to the neural network were the relative soil coverage and standard deviation of lightness.<br>The predicted leaf area and top fresh weight of test plug seedlings population based on the neural network model were fitted well with the measured values. Their coefficients of determination R2 were 0.95 and 0.94, respectively. The neural network model give much better result than the soil coverage models reported in the previous report.

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