A multi-region combined model for non-destructive prediction of soluble solids content in apple, based on brightness grade segmentation of hyperspectral imaging. (July 2019)

• Record Type: Journal Article
• Authors: Tian, Xi; Li, Jiangbo; Wang, Qingyan; Fan, Shuxiang; Huang, Wenqian; Zhao, Chunjiang
• Is Part Of: Biosystems engineering. Volume 183(2019)
• Page Start: 110
• Page End: 120
• Publication Date: 2019-07
• Subjects: Hyperspectral image -- Multi-region combined model -- Effective wavelength -- SSC -- Brightness grade segmentation; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2019.04.012
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : 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. Highlights: Multi-regions spectra were extracted with brightness grade segmentation method. A new calibration method integrated the spectra of Multi-regions was proposed. MCPLS models were build based on combination of variable number of LV.