VIS–NIR–SWIR Proximal Spectroradiometry Coupled with Machine Learning and Deep Learning for Ornamental Plant Identification and Classification

Abstract Non-destructive classification of ornamental plant material could improve greenhouse quality control, cultivar screening, and spectral phenotyping; however, most routine decisions still rely on visual inspection. We evaluated proximal VIS–NIR–SWIR spectroradiometry (400–2400 nm) for 900 balanced plant-level leaf or bract spectra representing nine ornamental classes from pothos, poinsettia, geranium, and hibiscus. The spectra formed a highly structured low-dimensional dataset, with the first three principal components explaining 97.11% of the total variance. Full-spectrum and edge-trimmed representations preserved high performance (best macro-F1 = 82.56% and 81.99%, respectively), whereas a ReliefF-selected 16-band green window (547–562 nm) reduced performance to 60.77% macro-F1. Among the full-spectrum deep models, MLP_Deep achieved 80.99% F1. These results show that proximal reflectance enables effective and interpretable plant-level ornamental phenotype classification and discrimination within the present benchmark, whereas compact green-band selection alone cannot replace broader VIS–NIR–SWIR information for closely related foliage classes.