Machine Learning-Based Spectral Analyses for <i>Camellia japonica</i> Cultivar Identification.

ABSTRACT: Camellia japonica is a plant species with high cultural and biological relevance. Besides being used as an ornamental plant species, C. japonica has relevant biological properties. Due to hybridization, thousands of cultivars are known, and their accurate identification is mandatory. Infrared spectroscopy is currently recognized as an accurate and rapid technique for species and/or subspecies identifications, including in plants. However, selecting proper analysis tools (spectra pre-processing, feature selection, and chemometric models) highly impacts the accuracy of such identifications. This study tests the impact of two distinct machine learning-based approaches for discriminating C. japonica cultivars using near-infrared (NIR) and Fourier transform infrared (FTIR)

SUBMITTER: Rodrigues PM 

PROVIDER: S-EPMC11821100 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature