A Residual Dense Lightweight Group Convolution Neural Network for Identifying the Gas Information of Different Levels of Tea-Reference-Cited by-同舟云学术

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A Residual Dense Lightweight Group Convolution Neural Network for Identifying the Gas Information of Different Levels of Tea

Published:2023-04-15 Issue:8 Volume:23 Page:8138-8145
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Yan Chaohua¹,Lu An²^ORCID,Song Dapeng³

Affiliation:

1. School of Digital Media and Art Design, Nanyang Institute of Technology, Nanyang, China

2. Chongqing Academy of Metrology and Quality Inspection, Chongqing, China

3. School of Medical Information Engineering, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China

Funder

General project of Chongqing Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10102602/10071959.pdf?arnumber=10071959

Reference26 articles.

1. E-nose Prototype to Monitoring the Growth and Maturation of Peaches in the Orchard

2. Development of a portable electronic nose based on a hybrid filter-wrapper method for identifying the Chinese dry-cured ham of different grades

3. Lightweight Residual Convolutional Neural Network for Soybean Classification Combined With Electronic Nose

4. A deep learning method combined with electronic nose to identify the rice origin

5. A Fast Pearson Graph Convolutional Network Combined With Electronic Nose to Identify the Origin of Rice

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4. An Adaptive Deep Learning Method Combined With an Electronic Nose System for Quality Identification of Soybeans Storage Period;IEEE Sensors Journal;2024-05-01

5. Identification of tea quality at different picking periods: A hyperspectral system coupled with a multibranch kernel attention network;Food Chemistry;2024-02

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