植物表面颜色与色素关系的神经网络建模

Zeng Qingmao, Zhang Tong, Zhu Tonglin
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引用次数: 0

摘要

将BP神经网络和支持向量机等智能算法与传统的化学方法相结合,建立了植物表面颜色与其色素之间的关系模型。利用上述构建的神经网络模型,人们可以通过获取相应的植物表面颜色信息来计算出植物色素的含量。与传统的建模方法相比,该方法可以显著节省时间和实验耗材。此外,它易于实现,因为它不需要接触样本,不会对样本造成任何损坏。该方法为植物色素的无损检测提供了实用的工具,为探索植物颜色的奥秘提供了解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Neural Network to Model the Relationship between Plant Surface Color and its Pigment
Combining the intelligent algorithm such as BP neural network and support vector maching (SVM) with traditional chemical method, this paper models the relationship between plant surface color and its pigment. Using the neural network model constructed above, people can figure out the content of plant pigments by getting the corresponding plant surface color information. Compared with the traditional modeling methods, this method can significantly save time and experimental supplies. Furthermore, it is easy to implement because it needn't touch samples and doesn't cause any damage to samples. So this method provides a practical tool for non-destructive measurements of plant pigments and solutions to explore the mystery of plant color.
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