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A Machine Learning Approach to Detect Dead Trees Caused by Longhorned Borer in Eucalyptus Stands Using UAV Imagery

Pest damages in eucalyptus plantations cause significant economic losses for the pulp and paper industry. Longhorned borers (ELB) outbreaks induce mortality in eucalyptus stands. In this study, multispectral imagery was obtained from unmanned aerial vehicles. We attempt to improve the classification process done in previous work. The local maxima of sliding a window and the Large-Scale Mean-Shift segmentation (LSMS) were applied to extract tree crows. Subsequently, the mean of spectral bands and twelve vegetation indices were calculated to characterize each segment. To classify tree canopies into dead and healthy trees, supervised machine learning using Random Forest (RF) and Support Vector Machine (SVM) were applied. The overall accuracy of Random Forests was 98.35% and Support Vector Machine of 97.7%. We concluded that SVM did not perform better than RF. Moreover, adding new vegetation indices in the classification process did not increase accuracy.