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  • 标题:Prediction of Moisture Content for Congou Black Tea Withering Leaves Using Image Features and Nonlinear Method
  • 本地全文:下载
  • 作者:Gaozhen Liang ; Chunwang Dong ; Bin Hu
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2018
  • 卷号:8
  • 期号:1
  • 页码:7854
  • DOI:10.1038/s41598-018-26165-2
  • 语种:English
  • 出版社:Springer Nature
  • 摘要:) and uniformity (U), which means that the extracted characteristics have great potential to predict the water contents. The performance parameters as correlation coefficient of prediction set (Rp), root-mean-square error of prediction (RMSEP), and relative standard deviation (RPD) of the SVM prediction model are 0.9314, 0.0411 and 1.8004, respectively. The non-linear modeling method can better describe the quantitative analytical relations between the image and water content. With superior generalization and robustness, the method would provide a new train of thought and theoretical basis for the online water content monitoring technology of automated production of black tea.
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