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A multi-region combined model for non-destructive prediction of soluble solids content in apple, based on brightness grade segmentation of hyperspectral imaging

文献类型： 外文期刊

作者： Tian, Xi 1 ; Li, Jiangbo 2 ; Wang, Qingyan 2 ; Fan, Shuxiang 2 ; Huang, Wenqian 2 ; Zhao, Chunjiang 1 ;

作者机构： 1.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

2.Beijing Res Ctr Intelligent Equipment Agr, 11 Shuguang Huayuan Middle Rd, Beijing 100097, Peoples R China

3.Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

4.Minist Agr, Key Lab Agriinformat, Beijing 100097, Peoples R China

5.Beijing Key Lab Intelligent Equipment Technol Agr, Beijing 100097, Peoples R China

关键词： Hyperspectral image; Multi-region combined model; Effective wavelength; SSC; Brightness grade segmentation

期刊名称：BIOSYSTEMS ENGINEERING （ 影响因子：4.123； 五年影响因子：4.508 ）

ISSN： 1537-5110

年卷期： 2019 年 183 卷

页码：

收录情况： SCI

摘要： On-line estimation of fruit internal attributes based on visible-near infrared spectrum is an effective approach for improving fruit value and farmer income. Brightness correction of the hyperspectral image can significantly affect the accuracy of quality estimation in fruits with spherical geometric structure, while pixel by pixel correction method is time-consuming and impractical in on-line rapid detection. To improve the flexibility and speed of the soluble solids content (SSC) estimation model of intact apple, the spectra of core region, middle region and outer region were extracted from hyperspectral reflectance imaging over the region of 400 -1000 nm with a brightness grade segmentation method, and then a multi-region combined partial least square (MCPLS) prediction model was built. Results showed that MCPLS method achieved better results than traditional PLS and multi-region average PLS methods. To further improve the applicability of the prediction model in practice, 21 wavelengths effective for SSC estimation were selected by successive projection algorithm and used to rebuild the prediction model using MCPLS method; the correlation coefficient, root mean square error of prediction set and residual predictive deviation were 0.9132, 0.3929 and 2.1652 respectively. Additionally, multi-region combined method just needs to compute the average spectra of each region, which significantly improved the detection speed by comparison with previous pixel by pixel brightness correction method. Hence the multi-region combined prediction model of SSC was developed based on the spectral contribution of each region to SSC prediction and the method of brightness grade segmentation. (C) 2019 IAgrE. Published by Elsevier Ltd. All rights reserved.

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