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A Machine Learning Approach to Detect Dead Trees Caused by Longhorned Borer in Eucalyptus Stands Using UAV Imagery
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Orientador(es)
Resumo(s)
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.
Descrição
Duarte, A., Borralho, N., & Caetano, M. (2021). A Machine Learning Approach to Detect Dead Trees Caused by Longhorned Borer in Eucalyptus Stands Using UAV Imagery. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium: Proceedings, 12 – 16 July, 2021 Virtual Symposium, Brussels, Belgium (pp. 5818-5821). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9554947 ----------------- We would like to thank Terradrone and all colleagues of the RAIZ team. The presented work was also carried out with a research project financed by the MySustainableForest project, which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 776045.
Palavras-chave
Eucalyptus stands Longhorned borer (ELB) machine learning unmanned aerial vehicles (UAV) Computer Science Applications General Earth and Planetary Sciences
Contexto Educativo
Citação
Editora
Institute of Electrical and Electronics Engineers (IEEE)