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Cluster Computing

, Volume 22, Supplement 6, pp 14661–14668 | Cite as

Establishment of feature set prediction model based on image processing technology

  • Xiaoyan QiaoEmail author
Article

Abstract

To build a prediction model of color feature set under image processing technology, a color feature extraction method based on image processing technology in two-dimensional images was developed. Taking the grapes as the experimental object, the rapid non-destructive testing method of pH value and soluble solids content (SSC) was put forward. The mean and variance of pixel gray value were extracted from the three color channel images of RGB, HIS, YIQ, YCbCr and HSV color space as color features. Then, a total of 120 color features were obtained. Based on the color feature, the least squares support vector machine was used to establish the grape pH value and the SSC detection model. The results showed that the correlation coefficient of the pH model was 0.870–0.886 and the correlation coefficient of the SSC model was 0.695–0.727 based on the different feature sets. It is concluded that the image color feature extraction can be applied to the grape pH value and SSC value of rapid non-destructive testing.

Keywords

Image processing Feature set Prediction model Color feature 

Notes

Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Nos. 61401255, 61771294).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mathematics and Information ScienceShandong Technology and Business UniversityYantaiChina
  2. 2.Co-innovation Center of Shandong Colleges and Universities: Future Intelligent ComputingYantaiChina

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