ResNet incorporating the fusion data of RGB & hyperspectral images improves classification accuracy of vegetable soybean freshness
Abstract The freshness of vegetable soybean (VS) is an important indicator for quality evaluation. Currently, deep learning-based image recognition technology provides a fast, efficient, and low-cost method for analyzing the freshness of food. The RGB (red, green, and blue) image recognition technol...
Main Authors: | Yuanpeng Bu, Jinxuan Hu, Cheng Chen, Songhang Bai, Zuohui Chen, Tianyu Hu, Guwen Zhang, Na Liu, Chang Cai, Yuhao Li, Qi Xuan, Ye Wang, Zhongjing Su, Yun Xiang, Yaming Gong |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2024-01-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-024-51668-6 |
Similar Items
-
COVID-ResNet: COVID-19 Recognition Based on Improved Attention ResNet
by: Tao Zhou, et al.
Published: (2023-03-01) -
Non-Destructive Detection of Soybean Pest Based on Hyperspectral Image and Attention-ResNet Meta-Learning Model
by: Jiangsheng Gui, et al.
Published: (2023-01-01) -
Stable ResNet
by: Hayou, S, et al.
Published: (2021) -
S-ResNet: An improved ResNet neural model capable of the identification of small insects
by: Pei Wang, et al.
Published: (2022-12-01) -
A Depthwise Separable Fully Convolutional ResNet With ConvCRF for Semisupervised Hyperspectral Image Classification
by: Yuxian Wang, et al.
Published: (2021-01-01)