Concepcion, Ronnie, IITurdiboev, ObidjonHail Mendigoria, ChristanCelep, FerhatBaikova, ElenaGemel Palconit, MariaRhay Vicerra, Ryan2025-01-212025-01-212021978-1-6654-9532-5https://doi.org/10.1109/TENCON54134.2021.9707455https://hdl.handle.net/20.500.12587/23754IEEE Region 10 Conference (TENCON) -- DEC 07-10, 2021 -- Auckland, NEW ZEALANDInvasive genetic and chemical-based laboratory techniques are very limited for in situ and in vivo applications especially in classifying leaf species in the wild. Out of 41 recorded Salvia species, 25 are endemic to the Central Asian region. In this study, a non-destructive model for profiling and identifying Salvia species (SalviaNet) was developed by employing computer vision allied with feature-based machine learning. The image set is composed of 25 Salvia species collected over Uzbekistan and other territories (Locus classicus) and photo-scanned to capture the totality of the leaf surface. CIELab thresholding was employed to fully segment the leaf pixels. Classification tree (CTree) was used to select the most significant spectro-textural-morphological leaf signatures resulting in only 11 attributes. These leaf signatures were profiled using the distance method with a distance power of 2. Hybrid CTree and linear discriminant analysis (CTree-LDA or SalviaNet) outperformed other computational models in classifying Salvia species based on the accuracy (90.7%) and sensitivity (90.7%). Based on profiling, leaf's red reflectance, compactness, and shape factor 2 are the strong determinants in discriminating Salvia species. Overall, the developed SalviaNet is proven reliable for on-site application and will essentially help the field of plant taxonomy.eninfo:eu-repo/semantics/closedAccessimage processing; machine learning; Salvia; species classification; plant taxonomyConference Object172210.1109/TENCON54134.2021.97074552-s2.0-85125964884Q3WOS:000799485900004N/A