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Non-destructive prediction of total soluble solids and titratable acidity in Kyoho grape using hyperspectral imaging and deep learning algorithm |
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| Authors: | Min Xu Jun Sun Jiehong Cheng Kunshan Yao Xiaohong Wu Xin Zhou |
| Affiliation: | 1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013 Jiangsu, China
School of Electronic Engineering, Changzhou College of Information Technology, Changzhou, 213164 Jiangsu, China Contribution: Conceptualization (lead), Data curation (lead), Methodology (lead), Software (lead), Writing - original draft (lead);2. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013 Jiangsu, China;3. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013 Jiangsu, China Contribution: Software (equal), Writing - review & editing (equal);4. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013 Jiangsu, China Contribution: Methodology (equal), Software (equal);5. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013 Jiangsu, China Contribution: Writing - review & editing (equal) |
| Abstract: | |
| Keywords: | Deep learning grape quality hyperspectral imaging non-destructive evaluation pixel-level features extraction stacked auto-encoders |