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Studies on quality evaluation of chrysanthemum cut flower (part 2) relation between experts' evaluation and morphological characteristics of cut flower

机译：菊花切花质量评价研究（二）专家评价与切花形态特征的关系

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摘要

In the previous installment of this series, the relationship between human being's evaluation and morphological features extracted from chrysanthemum cut flower was investigated in order to quantify the vague criteria that has been established based on human sense. It was also found that the individual morphological feature did not co-relate to human evaluation scores. It was considered that some combination of the features might improve the co-relation. The machine learning system such as the neural networks was considered to be usefully to automate the cut flower evaluation process.In this paper, length of cut flower, stem diameter, leaf area, length between flower and top leaf, leaf length, and, stem bend were selected for input parameters of neural networks whose output parameter was a human evaluation score. The neural networks were trained by KNT (Kalman Neuro Training)method. From the results, it was less than the human error resulted from the human double check procedure. It was also confirmed that the evaluation by the neural networks with several appropriate features was effective. In addition, a feasibility of automated cut flower evaluation sysetm, which does not involve human error, was found.
机译：在本系列的前一部分中，研究了人的评价与从菊花切花提取的形态特征之间的关系，以便量化基于人的感觉建立的模糊标准。还发现，个体形态特征与人类评价分数不相关。人们认为，这些特征的某种组合可能会改善相互关系。诸如神经网络之类的机器学习系统被认为对切花评估过程的自动化非常有用。本文中，切花长度，茎直径，叶面积，花与顶叶之间的长度，叶长度以及茎选择折弯作为神经网络的输入参数，其输出参数是人类评估得分。通过KNT（卡尔曼神经训练）方法训练神经网络。从结果来看，它小于人为双重检查程序导致的人为错误。还证实了通过具有几个适当特征的神经网络进行评估是有效的。另外，发现了一种不涉及人为错误的自动切花评估系统的可行性。

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