SELECTION OF BANDS FOR SECONDARY METABOLITES IN R. Arboreum USING HYPERSPECTRAL DATA

Monitoring and management of threatened, and medicinally important floral species in the complicated terrain are challenging and thus need advanced technological development. In this study, hyperspectral bands selected from radiometer data were used to identify sensitive bands for the marker compound at the genus level which could be upscaled to a larger scale for precise monitoring. Rhododendron is one of the keystone altitudinal species which is used for such purposes. Rhododendron arboreum flower is reported to be effective as a diuretic, choleretic, chronic diarrhoea, and anti-irritable bowel syndrome therapy. Its leaves are often used as a substitute for another famous plant drug Rohitaka. R. Arboreum, a tree species whose hyperspectral data were acquired in the complicated terrain in different regions of the Western Himalayas by sampling. Secondary metabolite (marker biomedicinal compound) was measured using chromatographic techniques and corresponding wavebands on their hyperspectral signature were identified using several sophisticated filtering techniques to denoise and select the desired feature with feature selection algorithms. Two band combination was developed using these identified wavebands for the secondary metabolite concentration estimation (Quer.) which provided satisfactory results for the same. Since these compounds are metabolic products, multiple aspects such as edaphic, biochemical, and topographic factors need to be assessed in determining the variation in the flavonoid content accordingly. Along with sensor-based hyperspectral band combination information was tested together with statistical unfolding approaches to understanding the influencing factors for flavonoids in the montane region. The result implies that R. arboreum developed band combinations based on selected wavebands only to determine the desired secondary metabolite concentration. On spectra, its values do not coincide with any other primary metabolite found in the R. arboreum. Hence the hyperspectral data are suitable for the estimation of phytochemical compounds. This will help in precise longterm mapping and monitoring of this species at a spatial scale without reciprocating any lab-scale technique each time.