Optimized the Spectral Reflectance Database of Herbal Plant Sample through ASD FieldSpc4 Spectroscopy

Unique spectral signatures are important for reflecting the inherent characteristics of materials. But different factors can alter the spectral signature, thus affecting measurement quality. Environmental and experimental conditions, atmosphericproperties, timing, orientation, height of measurement, FOV, calibration of spectral data and spectral averaging are some of the main factors that affect measurements. This paper presents a database collection method of performing spectral reflectance. The procedure involves calibration protocols, data acquisition routines, preprocessing methods, and storage in a structured database for retrieval and analysis efficiency. The spectral data are classified into plant species, environmental conditions, and measurement parameters for increased usability in machine learning and remote sensing studies. The derived database is a rich source of information for researchers in vegetation classification, stress identification, and hyperspectral modeling. Measuring the spectral signature using ASD fieldspec4 spectroradiometer hyperspectral non-imaging device. The research focus on the importance of optimized spectral data collection and management to aid new researcher’s decision making in environmental monitoring.