Using Hyperspectral Signatures for Predicting Foliar Nitrogen and Calcium Content of Tissue Cultured Little-leaf Mockorange (Philadelphus microphyllus A. Gray) Shoots

Abstract

Abstract Determining foliar mineral status of tissue cultured shoots can be costly and time consuming, yet hyperspectral signatures might be useful for determining mineral contents of these shoots. In this study, hyperspectral signatures were acquired from tissue cultured little-leaf mockorange (Philadelphus microphillus) shoots to determine the feasibility of using this technology to predict foliar nitrogen and calcium contents. After using a spectroradiometer to take hyperspectral images for determining foliar N and Ca contents, the correlation between the hyperspectral bands, vegetation indices, and hyperspectral features were calculated from the spectra. Features with high correlations were selected to develop the models via different regression methods including linear, random forest (RF), and support vector machines. The results showed that non-linear regression models developed through machine learning techniques, including RF methods and support vector machines provided satisfactory prediction models with high R2 values (%N by RF with R2 = 0.72, and %Ca by RF with R2 = 0.99), that can estimate nitrogen and calcium content of little-leaf mockorange shoots grown in vitro. Overall, the RF regression method provided the most accurate and satisfactory models for both foliar N and Ca estimation of little-leaf mockorange shoots grown in tissue culture.