Cargando…

A Lightweight convolutional neural network for nicotine prediction in tobacco by near-infrared spectroscopy

The content of nicotine, a critical component of tobacco, significantly influences the quality of tobacco leaves. Near-infrared (NIR) spectroscopy is a widely used technique for rapid, non-destructive, and environmentally friendly analysis of nicotine levels in tobacco. In this paper, we propose a n...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wang, Di, Zhao, Fengyuan, Wang, Rui, Guo, Junwei, Zhang, Cihai, Liu, Huimin, Wang, Yongsheng, Zong, Guohao, Zhao, Le, Feng, Weihua
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2023
Materias:	Plant Science
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10213436/ https://www.ncbi.nlm.nih.gov/pubmed/37251760 http://dx.doi.org/10.3389/fpls.2023.1138693

Ejemplares similares

Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks
por: Zhang, Lei, et al.
Publicado: (2020)

Just-in-Time Learning-Integrated Partial Least-Squares Strategy for Accurately Predicting 71 Chemical Constituents in Chinese Tobacco by Near-Infrared Spectroscopy
por: Liang, Youyan, et al.
Publicado: (2022)

Determination of plant silicon content with near infrared reflectance spectroscopy
por: Smis, Adriaan, et al.
Publicado: (2014)

Pepper leaf disease recognition based on enhanced lightweight convolutional neural networks
por: Dai, Min, et al.
Publicado: (2023)

Chemometric approach for discriminating tobacco trademarks by near infrared spectroscopy
por: Omar, Jone, et al.
Publicado: (2019)

Lightweight-Convolutional Neural Network for Apple Leaf Disease Identification
por: Fu, Lili, et al.
Publicado: (2022)

A lightweight method for maize seed defects identification based on Convolutional Block Attention Module
por: Li, Chao, et al.
Publicado: (2023)

Convolutional neural network in rice disease recognition: accuracy, speed and lightweight
por: Ning, Hongwei, et al.
Publicado: (2023)

Rapid estimation of nutritional elements on citrus leaves by near infrared reflectance spectroscopy
por: Galvez-Sola, Luis, et al.
Publicado: (2015)

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy
por: Vasseur, François, et al.
Publicado: (2022)

Predicting starch content in cassava fresh roots using near-infrared spectroscopy
por: Nkouaya Mbanjo, Edwige Gaby, et al.
Publicado: (2022)

Soluble Solids Content Binary Classification of Miyagawa Satsuma in Chongming Island Based on Near Infrared Spectroscopy
por: Chen, Yuzhen, et al.
Publicado: (2022)

Non-destructive Evaluation of the Quality Characteristics of Pomegranate Kernel Oil by Fourier Transform Near-Infrared and Mid-Infrared Spectroscopy
por: Okere, Emmanuel E., et al.
Publicado: (2022)

Hand-Held Near-Infrared Spectroscopy for Authentication of Fengdous and Quantitative Analysis of Mulberry Fruits
por: Yan, Hui, et al.
Publicado: (2019)

Erratum: A perspective on plant phenomics: Coupling deep learning and near-infrared spectroscopy
por: Frontiers Production Office,
Publicado: (2022)

Near-infrared spectroscopy for early selection of waxy cassava clones via seed analysis
por: Sousa, Massaine Bandeira e, et al.
Publicado: (2023)

LightMixer: A novel lightweight convolutional neural network for tomato disease detection
por: Zhong, Yi, et al.
Publicado: (2023)

Prediction and Utilization of Malondialdehyde in Exotic Pine Under Drought Stress Using Near-Infrared Spectroscopy
por: Zhang, Yini, et al.
Publicado: (2021)

Detection of Adulteration in Infant Formula Based on Ensemble Convolutional Neural Network and Near-Infrared Spectroscopy
por: Liu, Yisen, et al.
Publicado: (2021)

Discrimination and Content Analysis of Fritillaria Using Near Infrared Spectroscopy
por: Meng, Yu, et al.
Publicado: (2015)

Arabidopsis Seed Content QTL Mapping Using High-Throughput Phenotyping: The Assets of Near Infrared Spectroscopy
por: Jasinski, Sophie, et al.
Publicado: (2016)

Development and Validation of a New Methodology to Assess the Vineyard Water Status by On-the-Go Near Infrared Spectroscopy
por: Diago, Maria P., et al.
Publicado: (2018)

Identification of autism spectrum disorder based on functional near-infrared spectroscopy using adaptive spatiotemporal graph convolution network
por: Zhang, Haoran, et al.
Publicado: (2023)

Plant Species Classification Based on Hyperspectral Imaging via a Lightweight Convolutional Neural Network Model
por: Liu, Keng-Hao, et al.
Publicado: (2022)

A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize
por: Haque, Md. Ashraful, et al.
Publicado: (2022)

Determination of the Soluble Solids Content in Korla Fragrant Pears Based on Visible and Near-Infrared Spectroscopy Combined With Model Analysis and Variable Selection
por: Yang, Xuhai, et al.
Publicado: (2022)

Rapid determination of the total content of oleanolic acid and ursolic acid in Chaenomelis Fructus using near-infrared spectroscopy
por: Ming, Jing, et al.
Publicado: (2022)

Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves
por: Xiao, Qinlin, et al.
Publicado: (2022)

Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy
por: Chen, Rongqin, et al.
Publicado: (2023)

Non-destructive Measurements of Toona sinensis Chlorophyll and Nitrogen Content Under Drought Stress Using Near Infrared Spectroscopy
por: Liu, Wenjian, et al.
Publicado: (2022)

Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning
por: Zhang, Zheyu, et al.
Publicado: (2022)

Near-Infrared Spectroscopy in Bio-Applications
por: Beć, Krzysztof B., et al.
Publicado: (2020)

Subject-Independent Functional Near-Infrared Spectroscopy-Based Brain–Computer Interfaces Based on Convolutional Neural Networks
por: Kwon, Jinuk, et al.
Publicado: (2021)

Detection of Water pH Using Visible Near-Infrared Spectroscopy and One-Dimensional Convolutional Neural Network
por: Li, Dengshan, et al.
Publicado: (2022)

A Review of Machine Learning for Near-Infrared Spectroscopy
por: Zhang, Wenwen, et al.
Publicado: (2022)

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning
por: Chen, Yi, et al.
Publicado: (2021)

Functional near-infrared spectroscopy for intraoperative brain mapping
por: Qiu, Tianming, et al.
Publicado: (2019)

MGA-YOLO: A lightweight one-stage network for apple leaf disease detection
por: Wang, Yiwen, et al.
Publicado: (2022)

Development of a MAGIC population and high-resolution quantitative trait mapping for nicotine content in tobacco
por: Yuan, Guangdi, et al.
Publicado: (2023)

Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis
por: Hiwa, Satoru, et al.
Publicado: (2016)

Cannot write session to /tmp/vufind_sessions/sess_qbb59m7e1dmj65b5k7m03iamtk