Species classification from hyperspectral leaf information using machine learning approaches

Abstract

Information on plant species is fundamental to forest ecosystems, in the context of biodiversity monitoring and forest management. Traditional methods for plant species inventories are generally inefficient, in terms of cost and performance, and there is a high demand for a quick and feasible approach to be developed. Of the various attempts, remote sensing has emerged as an active approach for plant species classification, but most studies have concentrated on image processing and only a few of them ever use hyperspectral information, despite the wealth of information it contains. In this study, plant species are classified from hyperspectral leaf information using different machine learning models, coupled with feature reduction and selection methods, and their performance is optimized through Bayesian optimization. The results show that including feature selection and Bayesian optimization increases the classification accuracy of machine learning models. Among these, the Bayesian optimization-based support vector machine (SVM) model, combined with the recursive feature elimination (RFE) feature selection method, yields the best output, with an overall accuracy of 86% and a kappa coefficient of 0.85. Furthermore, the confusion matrix revealed that the number of samples correlates with classification accuracy. The support vector machine with informative bands after Bayesian optimization outperformed in classing plant species. The results of this study facilitate a better understanding of spectral (phenotype) information with plant species (genotype) and help to bridge hyperspectral information with ecosystem functions.