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Evaluation of apple inner quality based on improved deep belief network

Authors :
HU Chun-yan
YU Lai-hang
Source :
Shipin yu jixie, Vol 38, Iss 4, Pp 156-161,206 (2022)
Publication Year :
2022
Publisher :
The Editorial Office of Food and Machinery, 2022.

Abstract

Objective: In order to resolve a lot of redundant information and low precision of apple internal quality evaluation existed in apple near infrared spectroscopy, improving the precision of apple internal quality evaluation. Methods: A new apple inner quality evaluation model based on deep belief network (DBN) and grey wolf optimization algorithm was proposed. According to the characteristic of high dimension and complexity of apple spectral data, the method of selecting characteristic wavelengths of apple spectral data was determined by comparing the results of selecting characteristic wavelengths of full-band, principal component analysis and continuous projection. The parameters of DBN model were optimized by GWO Algorithm, and a continuous projection method for feature wavelength selection and GWO-DBN model for apple inner quality evaluation were proposed. Results: Compared with PSO-DBN, GA-DBN and DBN, the accuracy of apple inner quality evaluation based on GWO-DBN was the highest. Conclusion: This algorithm can effectively improve the accuracy of apple inner quality evaluation and provide a new method for apple inner quality evaluation.

Details

Language :
English, Chinese
ISSN :
10035788
Volume :
38
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Shipin yu jixie
Publication Type :
Academic Journal
Accession number :
edsdoj.0f01e2f810415ba7ac3d41b0a4279b
Document Type :
article
Full Text :
https://doi.org/10.13652/j.spjx.1003.5788.2022.90051