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.
Author(s): CIOCCIA, G.; MORAIS, C. P. de; BABOS, D. V.; MILORI, D. M. B. P.; ALVES, C. Z.; CENA, C.; NICOLODELLI, G.; MARANGONI, B. S.
Summary: 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 analysis. A total of 120 samples from two distinct cultivars of Brachiaria brizantha seeds exhibiting high vigor (HV) and low vigor (LV) in standard tests were studied. The raw LIBS spectra were normalized and submitted to outlier verification, previously to the reduction data dimensionality from principal component analysis. Supervised machine learning algorithm parameters were chosen by leave-oneout cross-validation in the test samples, and it was tested by external validation using a new set of data. The overall accuracy in external validation achieved 100% for HV and LV discrimination,regardless of the cultivar or the classification algorithm.
Publication year: 2022
Types of publication: Journal article
Unit: Embrapa Instrumentation
Keywords: Design of experiments, Discriminating, LIBS, Machine learning
Observation
Some of Embrapa's publications are published as ePub files. To read them, use or download one of the following free software options to your computer or mobile device. Android: Google Play Books; IOS: iBooks; Windows and Linux: Calibre.
Access other publications
Access the Agricultural Research Database (BDPA) to consult Embrapa's full library collection and records.
Visit Embrapa Bookstore to purchase books and other publications sold by Embrapa.