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Sensors 2018, 18(2), 605; https://doi.org/10.3390/s18020605

UAVs and Machine Learning Revolutionising Invasive Grass and Vegetation Surveys in Remote Arid Lands

1
Institute for Future Environments; Robotics and Autonomous Systems, Queensland University ofTechnology (QUT), 2 George St, Brisbane City QLD 4000, Australia
2
School of Mathematical Sciences; ARC Centre of Excellence for Mathematical & Statistical Frontiers(ACEMS), Queensland University of Technology (QUT), 2 George St, Brisbane City QLD 4000, Australia
3
Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall TR10 9FE, UK
*
Author to whom correspondence should be addressed.
Received: 30 November 2017 / Revised: 25 January 2018 / Accepted: 11 February 2018 / Published: 16 February 2018
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Abstract

The monitoring of invasive grasses and vegetation in remote areas is challenging, costly, and on the ground sometimes dangerous. Satellite and manned aircraft surveys can assist but their use may be limited due to the ground sampling resolution or cloud cover. Straightforward and accurate surveillance methods are needed to quantify rates of grass invasion, offer appropriate vegetation tracking reports, and apply optimal control methods. This paper presents a pipeline process to detect and generate a pixel-wise segmentation of invasive grasses, using buffel grass (Cenchrus ciliaris) and spinifex (Triodia sp.) as examples. The process integrates unmanned aerial vehicles (UAVs) also commonly known as drones, high-resolution red, green, blue colour model (RGB) cameras, and a data processing approach based on machine learning algorithms. The methods are illustrated with data acquired in Cape Range National Park, Western Australia (WA), Australia, orthorectified in Agisoft Photoscan Pro, and processed in Python programming language, scikit-learn, and eXtreme Gradient Boosting (XGBoost) libraries. In total, 342,626 samples were extracted from the obtained data set and labelled into six classes. Segmentation results provided an individual detection rate of 97% for buffel grass and 96% for spinifex, with a global multiclass pixel-wise detection rate of 97%. Obtained results were robust against illumination changes, object rotation, occlusion, background cluttering, and floral density variation. View Full-Text
Keywords: biosecurity; buffel grass; Cenchrus ciliaris; drones; remote surveillance; spinifex; Triodia sp.; unmanned aerial vehicles (UAV); vegetation assessments; xgboost biosecurity; buffel grass; Cenchrus ciliaris; drones; remote surveillance; spinifex; Triodia sp.; unmanned aerial vehicles (UAV); vegetation assessments; 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.; Gonzalez, F.; Mengersen, K.; Gaston, K.J. UAVs and Machine Learning Revolutionising Invasive Grass and Vegetation Surveys in Remote Arid Lands. Sensors 2018, 18, 605.

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