Automated grapevine cultivar classification based on machine learning using leaf morpho-colorimetry, fractal dimension and near-infrared spectroscopy parameters

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2018.06.035 ◽

2018 ◽

Vol 151 ◽

pp. 311-318 ◽

Cited By ~ 13

Author(s):

S. Fuentes ◽

E. Hernández-Montes ◽

J.M. Escalona ◽

J. Bota ◽

C. Gonzalez Viejo ◽

...

Keyword(s):

Machine Learning ◽

Fractal Dimension ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Grapevine Cultivar ◽

Cultivar Classification

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Automated Chinese medicinal plants classification based on machine learning using leaf morpho-colorimetry, fractal dimension and visible/near infrared spectroscopy

International Journal of Agricultural and Biological Engineering ◽

10.25165/j.ijabe.20191202.4637 ◽

2019 ◽

Vol 12 (2) ◽

pp. 123-131

Author(s):

Jinru Xue ◽

◽

Sigfredo Fuentes ◽

Carlos Poblete-Echeverria ◽

Claudia Gonzalez Viejo ◽

...

Keyword(s):

Machine Learning ◽

Fractal Dimension ◽

Infrared Spectroscopy ◽

Medicinal Plants ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Chinese Medicinal Plants

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Improving the accuracy of soil organic carbon content prediction based on visible and near-infrared spectroscopy and machine learning

Environmental Earth Sciences ◽

10.1007/s12665-021-09582-x ◽

2021 ◽

Vol 80 (8) ◽

Author(s):

Mingxing Xu ◽

Xianyao Chu ◽

Yesi Fu ◽

Changjiang Wang ◽

Shaohua Wu

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Content ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Organic Carbon Content ◽

Soil Organic Carbon Content

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Predicting soil phosphorus and studying the effect of texture on the prediction accuracy using machine learning combined with near-infrared spectroscopy

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy ◽

10.1016/j.saa.2020.118736 ◽

2020 ◽

Vol 242 ◽

pp. 118736

Author(s):

Rabie Reda ◽

Taoufiq Saffaj ◽

Salah Eddine Itqiq ◽

Ilham Bouzida ◽

Ouadi Saidi ◽

...

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Prediction Accuracy ◽

Near Infrared ◽

Soil Phosphorus

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A Machine Learning Approach to Identify Functional Biomarkers for Traumatic Brain Injury (TBI) Using Functional Near-Infrared Spectroscopy (fNIRS)

Biomedical Optics 2016 ◽

10.1364/brain.2016.bth4d.2 ◽

2016 ◽

Cited By ~ 1

Author(s):

N Karamzadeh ◽

F Amyot ◽

K Kenney ◽

F Chowdhry ◽

A Anderson ◽

...

Keyword(s):

Machine Learning ◽

Traumatic Brain Injury ◽

Infrared Spectroscopy ◽

Brain Injury ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Learning Approach ◽

Functional Near Infrared Spectroscopy ◽

Machine Learning Approach ◽

Functional Biomarkers

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Machine Learning Calibration for Near Infrared Spectroscopy Data: A Visual Programming Approach

Lecture Notes in Electrical Engineering - Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019 ◽

10.1007/978-981-15-5281-6_40 ◽

2020 ◽

pp. 577-590

Author(s):

Mahmud Iwan Solihin ◽

Zheng Zekui ◽

Chun Kit Ang ◽

Fahri Heltha ◽

Mohamed Rizon

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Visual Programming ◽

Programming Approach ◽

Spectroscopy Data

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Improving the diagnostic accuracy for major depressive disorder using machine learning algorithms integrating clinical and near-infrared spectroscopy data

Journal of Psychiatric Research ◽

10.1016/j.jpsychires.2022.01.026 ◽

2022 ◽

Author(s):

Cyrus SH. Ho ◽

Y.L. Chan ◽

Trevor WK. Tan ◽

Gabrielle WN. Tay ◽

T.B. Tang

Keyword(s):

Machine Learning ◽

Major Depressive Disorder ◽

Infrared Spectroscopy ◽

Depressive Disorder ◽

Diagnostic Accuracy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Machine Learning Algorithms ◽

Spectroscopy Data ◽

Major Depressive

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Near-infrared spectroscopy and machine learning-based classification and calibration methods in detection and measurement of anionic surfactant in milk

Journal of Food Composition and Analysis ◽

10.1016/j.jfca.2021.104170 ◽

2021 ◽

Vol 104 ◽

pp. 104170

Author(s):

Elahesadat Hosseini ◽

Jahan B. Ghasemi ◽

Bahram Daraei ◽

Gholamhassan Asadi ◽

Nooshin Adib

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Anionic Surfactant ◽

Near Infrared ◽

Calibration Methods

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Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms

Journal of the Science of Food and Agriculture ◽

10.1002/jsfa.8506 ◽

2017 ◽

Vol 98 (2) ◽

pp. 618-627 ◽

Cited By ~ 26

Author(s):

Claudia Gonzalez Viejo ◽

Sigfredo Fuentes ◽

Damir Torrico ◽

Kate Howell ◽

Frank R Dunshea

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Infrared Spectroscopy ◽

Chemical Composition ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Learning Algorithms ◽

Machine Learning Algorithms

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Prediction of pellet quality through machine learning techniques and near-infrared spectroscopy

Computers & Industrial Engineering ◽

10.1016/j.cie.2020.106566 ◽

2020 ◽

Vol 147 ◽

pp. 106566 ◽

Cited By ~ 1

Author(s):

Manuela Mancini ◽

Alex Mircoli ◽

Domenico Potena ◽

Claudia Diamantini ◽

Daniele Duca ◽

...

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Machine Learning Techniques ◽

Pellet Quality ◽

Learning Techniques

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Rapid discrimination of coal geographical origin via near-infrared spectroscopy combined with machine learning algorithms

Infrared Physics & Technology ◽

10.1016/j.infrared.2019.103180 ◽

2020 ◽

Vol 105 ◽

pp. 103180 ◽

Cited By ~ 1

Author(s):

Xinhui Yu ◽

Weidong Guo ◽

Nan Wu ◽

Liang Zou ◽

Meng Lei

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Geographical Origin ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Rapid Discrimination

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