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Sensors 2018, 18(4), 944; https://doi.org/10.3390/s18040944

Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence

1
Insitute for Future Environments; Robotics and Autonomous Systems, Queensland University of Technology (QUT), 2 George St, Brisbane City, QLD 4000, Australia
2
Horticulture & Forestry Science, Department of Agriculture & Fisheries, Ecosciences Precinct, 41 Boggo Rd Dutton Park, QLD 4102, Australia
3
BioProtection Technologies, The New Zealand Institute for Plant & Food Research Limited, Gerald St, Lincoln 7608, New Zealand
*
Author to whom correspondence should be addressed.
Received: 22 February 2018 / Revised: 17 March 2018 / Accepted: 21 March 2018 / Published: 22 March 2018
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Abstract

The environmental and economic impacts of exotic fungal species on natural and plantation forests have been historically catastrophic. Recorded surveillance and control actions are challenging because they are costly, time-consuming, and hazardous in remote areas. Prolonged periods of testing and observation of site-based tests have limitations in verifying the rapid proliferation of exotic pathogens and deterioration rates in hosts. Recent remote sensing approaches have offered fast, broad-scale, and affordable surveys as well as additional indicators that can complement on-ground tests. This paper proposes a framework that consolidates site-based insights and remote sensing capabilities to detect and segment deteriorations by fungal pathogens in natural and plantation forests. This approach is illustrated with an experimentation case of myrtle rust (Austropuccinia psidii) on paperbark tea trees (Melaleuca quinquenervia) in New South Wales (NSW), Australia. The method integrates unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Imagery is acquired using a Headwall Nano-Hyperspec ® camera, orthorectified in Headwall SpectralView ® , and processed in Python programming language using eXtreme Gradient Boosting (XGBoost), Geospatial Data Abstraction Library (GDAL), and Scikit-learn third-party libraries. In total, 11,385 samples were extracted and labelled into five classes: two classes for deterioration status and three classes for background objects. Insights reveal individual detection rates of 95% for healthy trees, 97% for deteriorated trees, and a global multiclass detection rate of 97%. The methodology is versatile to be applied to additional datasets taken with different image sensors, and the processing of large datasets with freeware tools. View Full-Text
Keywords: Austropuccinia psidii; drones; hyperspectral camera; machine learning; Melaleuca quinquenervia; myrtle rust; non-invasive assessment; paperbark; unmanned aerial vehicles (UAV); xgboost Austropuccinia psidii; drones; hyperspectral camera; machine learning; Melaleuca quinquenervia; myrtle rust; non-invasive assessment; paperbark; unmanned aerial vehicles (UAV); xgboost
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sandino, J.; Pegg, G.; Gonzalez, F.; Smith, G. Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence. Sensors 2018, 18, 944.

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