Estimation of Biochemical Compounds in Tradescantia Leaves Using VIS-NIR-SWIR Hyperspectral and Chlorophyll a Fluorescence Sensors

Abstract

An integrated approach that utilises hyperspectral and chlorophyll a fluorescence sensors to predict biochemical and biophysical parameters represents a new generation of remote-sensing research. The main objective of this study was to obtain a detailed spectral profile that correlates with plant physiology, thereby enhancing our understanding and management of plant health, pigment profiles, and compound fingerprints. Leveraging datasets using non-imaging or passive hyperspectral and chlorophyll fluorescence sensors to collect data in Tradescantia species demonstrated significant differences in leaf characteristics with pigment concentrations and structural components. The main goal was to use principal component analysis (PCA) and partial least squares regression (PLS) methods to analyse the variations in their spectra. Our findings demonstrate a strong correlation between hyperspectral data and chlorophyll fluorescence, which is further supported by the development of hyperspectral vegetation indices (HVIs) that can accurately evaluate fingerprints and predict many compounds in variegated leaves. The higher the integrated analytical approach and its potential application in HVIs and fingerprints, the better the selection of wavelengths and sensor positions for rapid and accurate analysis of many different compounds in leaves. Nonetheless, limitations arose from the specificity of the data for the Tradescantia species, warranting further research across diverse plant types and compounds in the leaves. Overall, this study paves the way for more sustainable and informed agricultural practices through breakthroughs in the application of sensors to remote-sensing technologies.

Keywords

• biochemical compounds
• HVI
• hyperspectral sensors
• partial least squares regression
• principal component analysis
• purple leaves