Vis/NIR Spectroscopy and Chemometrics for Non-Destructive Estimation of Chlorophyll Content in Different Plant Leaves

Vegetation biochemical and biophysical variables, especially chlorophyll content, are pivotal indicators for assessing drought's impact on plants. Chlorophyll, crucial for photosynthesis, ultimately influences crop productivity. This study evaluates the mean squared Euclidean distance (MSD) method, traditionally applied in soil analysis, for estimating chlorophyll content in five diverse leaf types across various months using visible/near-infrared (vis/NIR) spectral reflectance. The MSD method serves as a tool for selecting a representative calibration dataset. By integrating MSD with partial least squares regression (PLSR) and the Cubist model, we aim to accurately predict chlorophyll content, focusing on key spectral bands within the ranges of 500-640 nm and 740-1100 nm. In the validation dataset, PLSR achieved a high determination coefficient (R²) of 0.70 and a low mean bias error (MBE) of 0.04 mg g^-1. The Cubist model performed even better, demonstrating an R² of 0.77 and an exceptionally low MBE of 0.01 mg g^-1. These results indicate that the MSD method serves as a tool for selecting a representative calibration dataset in leaves, and vis/NIR spectrometry combined with the MSD method is a promising alternative to traditional methods for quantifying chlorophyll content in various leaf types over various months. The technique is non-destructive, rapid, and consistent, making it an invaluable tool for assessing drought impacts on plant health and productivity.