Developing optimized spectral indices using machine learning to model Fusarium circinatum stress in Pinus radiata seedlings

Abstract

Narrowband normalized difference spectral indices (SI) have found wide application in vegetation studies. Consequently, several studies have investigated the utility of optimized SI for targeted applications. The objective of this study is to statistically develop optimized two-band normalized difference SI from a subset of hyperspectral bands derived using the Boruta wrapper algorithm. These indices are applied to model Fusarium circinatum stress in Pinus radiata seedlings. The performance of our developed optimized indices was compared with a selection of widely used existing SI ( n = 111) noted in the literature. Analyses were undertaken within a univariate (using the Jeffries–Matusita distance) and a multivariate (using the random forest algorithm) framework. Our results clearly demonstrate the improved accuracies using optimized SI (overall accuracy ranged from 76% to 96%) compared with using existing indices (overall accuracy ranged from 83% to 90%). Additionally, our results show that a multivariate approach yields superior results compared with a univariate approach. Overall, the results demonstrate the operational potential of optimized two-band normalized difference SI within a multivariate paradigm.