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

Abstract

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 2 3 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-one-out 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.

Plant phenotyping relevance

LIBSによる種子活力の取得・識別条件を実験計画法で最適化し、機械学習分類を外部検証しており、植物形質測定手法が研究の中心である。

Code and data availability

The paper describes LIBS spectra of 120 Brachiaria seed samples analyzed with Python/Scikit-Learn ML, but no public data or code deposit is mentioned. The only supplement contains PC loading figures, not datasets or code. No authors' URL for data or scripts is provided.

No evidence-backed public reproduction asset is currently recorded.