Impruved prediction of soluble solid content of apple using a combination of spectral and textural features of hyperspectral images
Abstract
We established prediction models based on the combination of spectral and different advanced image features to improve the prediction accuracy of solid-soluble content (SSC) of apple. Eight optimal wavelengths were selected using a new variable selection method called variable combination population analysis (VCPA). Image textural features of the first three principal component score images were obtained using a gray level co-occurrence matrix (GLCM) and a local binary pattern (LBP). Next, a random frog algorithm was developed to select optimal textural features for further analysis. A support vector regression (SVR) model based on spectral and different textural features was developed to predict the SSC of the apple. The model based on eight optimal wavelengths and nine optimal GLCM features of principal component images yielded the best result with the determination coefficient for prediction (Rp2) of 0.9193, root mean square error for prediction (RMSEP) of 0.2955, and the ratio of the standard deviation of the prediction set to the root mean square error of prediction (RPD) with a value of 3.50. These results revealed that the spectral combined with optimal GLCM features from principal component images coupled with the SVR model has the potential for prediction of the SSC of apple.
Supporting agencies: The authors are grateful to Sichuan Province Department of Education (China) for support through the program of “Research on Apple Quality Nondestructive Testing Method Based on Hyperspectral Image Technology” (17ZB0333). This work was also supported by the Lab of Agricultural Information Engineering, Sichuan Key Laboratory. This study was funded by Natural Science Program of Sichuan Education Department (17ZB0333).
About the Authors
T. Pang
College of Mechanical and Electrical Engineering at Sichuan Agricultural University
China
Yaan 625014.
China
Yaan 625014.
L. Rao
College of Mechanical and Electrical Engineering at Sichuan Agricultural University
China
Yaan 625014.
China
Yaan 625014.
X. Chen
College of Information and Engineering at Sichuan Agricultural University
China
Yaan 625014.
China
Yaan 625014.
J. Cheng
College of Mechanical and Electrical Engineering at Sichuan Agricultural University
China
Yaan 625014.
China
Yaan 625014.
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Review
For citations:
Pang T., Rao L., Chen X., Cheng J. Impruved prediction of soluble solid content of apple using a combination of spectral and textural features of hyperspectral images. Zhurnal Prikladnoii Spektroskopii. 2020;87(6):1024(1)-1024(10).
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