Laser-Induced Breakdown Spectroscopy Associated with the Design of Experiments and Machine Learning for Discrimination of Brachiaria brizantha Seed Vigor

Laser-Induced Breakdown Spectroscopy Associated with the Design of Experiments and Machine Learning for Discrimination of Brachiaria brizantha Seed Vigor

Laser-induced breakdown spectroscopy (LIBS) associated with machine learning algorithms (ML) was used to evaluate the Brachiaria seed physiological quality by discriminating the high and low vigor seeds. A 23 factorial design was used to optimize the LIBS experimental parameters for spectral analysi...

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Bibliographic Details
Main Authors: Alves, C.Z (Author), Babos, D.V (Author), Cena, C. (Author), Cioccia, G. (Author), Marangoni, B.S (Author), Milori, D.M.B.P (Author), Nicolodelli, G. (Author), Pereira de Morais, C. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Atomic emission spectroscopy
Brachiaria brizantha
brachiaria seed
Brachiaria seed
design of experiments
Design of experiments
discriminating
Discriminating
Experiment learning
Experimental parameters
Factorial design
Laserinduced breakdown spectroscopy (LIBS)
Learning algorithms
LIBS
machine learning
Machine learning algorithms
Machine-learning
Principal component analysis
Quality control
Spectrum analysis
Standard tests
Supervised learning
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