Nondestructive detection of yellow peach quality parameters based on 3D-CNN and hyperspectral images. (November 2020)

• Record Type: Journal Article
• Authors: Xu, Shipu; Liu, Yong; Hu, Wenwen; Wu, Yingjing; Liu, Sijia; Wang, Yunsheng; Liu, Chang
• Is Part Of: Journal of physics. Volume 1682(2020)
• Publication Date: 2020-11
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1682/1/012030
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In recent years, efficient and accurate detection of fruit quality has become an important means of non-destructive testing of fruit quality. In order to solve the problems of cost, efficiency and accuracy in the non-destructive testing of yellow peach quality. This paper proposes a new method for simultaneous detection of yellow peach hyperspectral multiple quality parameters. This method uses the method of extracting and recombining wavelengths at equal intervals in the full-band spectrum instead of the characteristic wavelength selection method. It can make the hyperspectral image contain all-band spectral information. This method uses 3D-CNN to replace the original regression modelling method, which improves the accuracy of model prediction. This method uses the shared network convolutional layer method to perform multi-task learning on the sugar content and hardness of yellow peaches, and finally realizes the simultaneous detection of multiple quality parameters of yellow peaches.