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首页> 外文期刊>配管·装置·プラント技術 >Non-destructive growth measurement of cabbage plug seedlings population by image information part 2 growth measurement by neural network model
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Non-destructive growth measurement of cabbage plug seedlings population by image information part 2 growth measurement by neural network model

机译：利用图像信息第2部分无损生长测量卷心菜插秧种群的神经网络模型

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摘要

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.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 R{sup}2 were 0.95 and 0.94, respectively. The neural networkmodel give much better result than the soil coverage models reported in the previous report.
机译：这项研究的目的是利用其图像信息对插秧苗种群进行无损生长测量。在本报告中，开发了用于无损测量卷心菜插秧幼苗叶片面积和顶部鲜重的神经网络模型。神经网络的输入是相对土壤覆盖率和明度的标准偏差。基于神经网络模型的试验塞苗种群的预测叶面积和最高鲜重与测量值非常吻合。它们的确定系数R {sup} 2分别为0.95和0.94。神经网络模型比以前的报告中的土壤覆盖模型提供了更好的结果。

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