Spectroscopic Method for Detection of Soluble Solid Content in Cherry Tomato Using Deep Convolutional Generative Adversarial Network-Based Data Augmentation
Considering insufficient sample numbers in the practical detection of soluble solid content (SSC) in cherry tomato, we proposed a deep convolutional generation adversarial network (DCGAN) model to expand spectral data and SSC label data, and established a one-dimensional convolutional neural network...
Saved in:
Main Author: | WU Zhijing, LIU Fuqiang, LI Zhigang, CHEN Hui |
---|---|
Format: | Article |
Language: | English |
Published: |
China Food Publishing Company
2025-01-01
|
Series: | Shipin Kexue |
Subjects: | |
Online Access: | https://www.spkx.net.cn/fileup/1002-6630/PDF/2025-46-2-024.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Advances in generative adversarial network
by: Wanliang WANG, et al.
Published: (2018-02-01) -
Preparation and Efficacy of Microemulsion Carvacrol-Based Fruit and Vegetable Cleaner and Its Application on Cherry Tomatoes
by: Yanshuo Wang, et al.
Published: (2025-01-01) -
Stock price prediction with attentive temporal convolution-based generative adversarial network
by: Ying Liu, et al.
Published: (2025-03-01) -
Liquid smoke-infused edible coatings: Antimicrobial agents for preserving cherry tomatoes
by: Muhammad Faisal, et al.
Published: (2025-06-01) -
Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS
by: M. N. Afzal Khan, et al.
Published: (2025-01-01)