Utilizing wasserstein generative adversarial networks for enhanced hyperspectral imaging: A novel approach to predict soluble sugar content in cherry tomatoes-Reference-Cited by-同舟云学术

Utilizing wasserstein generative adversarial networks for enhanced hyperspectral imaging: A novel approach to predict soluble sugar content in cherry tomatoes

Published:2024-08 Issue: Volume:206 Page:116585
ISSN:0023-6438
Container-title:LWT
language:en
Short-container-title:LWT

Author:

Cui Jiarui,Zhang Yao,hao Jie,ma Yan,Men Jiali,pan Shibo,Wu Longguo

Funder

Chongqing Municipal Key Research and Development Program of China

Ningxia University

Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province

Chongqing Municipal Youth Science and Technology Talent Training Project

Key Research and Development Program of Zhejiang Province

Science and Technology Project of State Grid

Natural Science Foundation of Ningxia Province

Publisher

Elsevier BV

Reference33 articles.

1. Fruit quality and defect image classification with conditional GAN data augmentation;Bird;Scientia Horticulturae,2022

2. GAN-AE: An unsupervised intrusion detection system for MQTT networks;Boppana;Engineering Applications of Artificial Intelligence,2023

3. MTD-YOLO: Multi-task deep convolutional neural network for cherry tomato fruit bunch maturity detection;Chen;Computers and Electronics in Agriculture,2024

4. Identification of near geographical origin of wolfberries by a combination of hyperspectral imaging and multi-task residual fully convolutional network;Cui;Foods,2022

5. Development of a new hyperspectral imaging technology with autoencoder-assisted generative adversarial network for predicting the content of polyunsaturated fatty acids in red meat;Cui;Computers and Electronics in Agriculture,2024

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research on Default Classification of Unbalanced Credit Data Based on PixelCNN-WGAN;Electronics;2024-08-28