Quantification and mapping of medicinally important Quercitrin compound using hyperspectral imaging and machine learning

• Spectral analysis and machine learning techniques were used for waveband selection and biochemical parameter retrieval. • Developed hyperspectral Indices was found to be the most sensitive and accurate for Quercitrin estimation. • For large scale mapping of Quercitrin, PRISMA hyperspectral imagery was used and validated. Precise spatial mapping of individual species using hyperspectral data is crucial for effective forest management and policy-making. This study focuses on Rhododendron arboreum , known for its medicinal properties attributed to the flavonoid Quercitrin. Sample data and spectroradiometer data were collected from the complex terrain of the Kumaon region in the Himalayas. Hyperspectral data, which includes signal variations based on biophysical and biochemical properties along with noise, were preprocessed using filtering techniques to enhance signal clarity by removing noise. Smoothing techniques were applied to remove noisy bands from the spectra, such as the Savitzky-Golay filter for reduced least square fit complexity and the Average Mean filter for taking mean spectral values. Subsequently, Spectral Analysis (SA) techniques, including first derivative, second derivative, and continuum removal, were employed. These mathematical transformations highlighted absorption troughs and determined the effect of Quercitrin on spectral wavelengths. Principal Component Analysis (PCA) was used to identify the most relevant bands related to Quercitrin. Additionally, regression analysis was applied on resampled spectral data, selected significant wavelengths based on variable importance values, pinpointing the most prominent wavelengths: 1196, 1229, 1328, 1383, 1425, 1636, 1661, 1699, 1785, and 1715 nm. Over 50 two-band combination indices were tested, and those with p-values less than 0.05 were deemed significant. For the development of prediction model, Machine Learning (ML) algorithms, including Support Vector Machine (SVM), Relevance Vector Machine (RVM), Random Forest (RF), and Artificial Neural Network (ANN), were applied. The Random Forest model, which splits input data into trees to simulate the best model based on observed values, demonstrated high effectiveness in predicting Quercitrin levels, achieving a training correlation of 0.864 and a testing correlation of 0.570. Hence RF proved to be a best technique of band selection as well as robust for Quercitrin prediction. This methodological approach highlights the importance of advanced data processing and analysis techniques in remote sensing applications for forest phytochemical prediction.