Study of near infrared spectrum classification for tobacco leaf position based on deep belief network

Jing, 王 静 Wang; Xiangqian, 丁香乾 Ding; Xiaodong, 王晓东 Wang; Feng, 韩 凤 Han; Dong, 韩 冬 Han; Xiaona, 曲晓娜 Qu

doi:10.3788/irla201948.0404001

红外与激光工程

2019

DOI: 10.3788/irla201948.0404001

|View full text |Cite

Study of near infrared spectrum classification for tobacco leaf position based on deep belief network

王静 Wang Jing¹,

丁香乾 Ding Xiangqian²,

王晓东 Wang Xiaodong³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Review Of Near Infrared Spectrum Detection Based on Deep Learning

Liu¹

2023

HSET

View full text Add to dashboard Cite

Near infrared spectroscopy technology has the characteristics of accurate analysis of material composition and characteristics, quantitative testing, non-destructive testing, and so on. With the progress of artificial intelligence and the development of deep learning technology, near-infrared spectroscopy detection systems based on deep learning algorithms have emerged, which include various typical methods and have achieved good application results in food safety monitoring, pollutant detection, and drug analysis. This article provides a systematic review of domestic and foreign literature, introducing the principles, characteristics, and development history of deep learning and near-infrared spectroscopy technology. It discusses the research significance of near-infrared spectroscopy detection based on deep learning. It also reviews the latest progress in deep learning based near-infrared spectroscopy related technologies, elaborates on the advantages, disadvantages, and applicable fields of the methods, and makes prospects and predictions for the future development trends in this field.

show abstract

Review Of Near Infrared Spectrum Detection Based on Deep Learning

Liu¹

2023

HSET

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Study of near infrared spectrum classification for tobacco leaf position based on deep belief network

Cited by 1 publication

References 0 publications

Review Of Near Infrared Spectrum Detection Based on Deep Learning

Review Of Near Infrared Spectrum Detection Based on Deep Learning

Contact Info

Product

Resources

About