Using Boruta-Selected Spectroscopic Wavebands for the Asymptomatic Detection of Fusarium Circinatum Stress

Abstract

High spectral resolution multitemporal data were used to model asymptomatic stress caused by Fusarium circinatum in 3-month old Pinus radiata seedlings. The objectives of the study were: 1) to identify an optimal subset of wavebands that could model asymptomatic stress in P. radiata seedlings and 2) to develop a robust classification model for discriminating healthy and stressed seedlings. To achieve these objectives, spectral data were collected for healthy, infected, and damaged seedlings using a hand-held field spectroradiometer. The data were analyzed, first for combined classes and then for class pairs using the Boruta algorithm. Results indicated that the best discrimination was possible at week three for all classes, with a KHAT value of 0.79 and an out of bag error of 14.00% (CV error = 16.00%), using a subset of 107 wavebands. A closer examination of the class pairs, namely healthy-infected (H-I) and infected-damaged (I-D), showed improved discrimination with KHAT values of 0.82 and 0.84, respectively. The H-I class pair was classified using a subset of just 38 wavebands, whereas the I-D class pair was classified using a subset of just 40 wavebands. Overall, this study demonstrated that it is more difficult to discriminate asymptomatic stress when additional stress related classes are present. Nonetheless, the methodology developed in this study has the potential to be operationalized within a nursery environment for the early detection of F. circinatum-induced stress in P. radiata seedlings.