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Autonomous Remote Sensing of Invasive Species from Robotic Aircraft

  • Ali Haydar Göktoǧan
  • Salah Sukkarieh
Reference work entry

Abstract

This chapter presents an Unmanned Aircraft System (UAS) which consists of a helicopter, a fixed wing Unmanned Aerial Vehicles (UAVs), and their supporting infrastructure. This UAS, which was used in a number of environmental research experiments focused on autonomous remote sensing, detection, classification, and management of invasive species, weeds, in Australia.

The annual cost of weeds to the Australian economy is estimated at A$4 billion. Over the last few years, experiments have been performed at three geographically distant regions of Australia to evaluate if the UAS can be used as a cost-effective tool in management of invasive species. In these experiments three distinct families of weeds, growing on three different types of terrain were investigated.

In the first group of experiments, a helicopter UAV equipped with a high-resolution imaging payload was flown over difficult to reach water channels and wetlands for detection of aquatic weeds. The second set of experiments was performed in large, relatively flat rangelands to map woody weed infestations. A low-flying fixed-wing UAV platform was also operated over a designated cattle grazing area and collected high-resolution aerial imagery. Weed infestation maps were produced from aerial imagery using machine learning techniques. By incorporating the human subject matter experts and farmers into the decision-making process, weed management plans produced. An autonomous helicopter was then tasked with spraying herbicides on aquatic weeds and dispensing granular herbicides on top of the selected woody weeds. The third set of experiments was focused on the airborne detection of wheel cacti on remote mountainous terrain using fixed-wing aircraft. Cacti infestation maps were generated and compared with data collected by weed experts on the ground. Successful results of these experiments are encouraging and suggest that robotic aircrafts in a properly designed UAS can play an important role in environmental robotic science.

Keywords

Global Position System Data Aquatic Weed Unman Aircraft System Aerial Imagery Above Ground Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work is supported in part by the Australian Centre for Field Robotics (ACFR) funded by the New South Wales State Government and Land & Water Australia (LWA) as a part of the “Defeating the Weed Menace” (DWM) program; by Meat and Livestock Australia (MLA) under project code B.NBP.0474; “UAV Surveillance Systems for the Management of Woody Weeds,” the Australian Weeds Research Council (AWRC) under project code AWRC08-04; the ARC Centre of Excellence program and Linkage Project LP0989291, funded by the Australian Research Council (ARC); and the New South Wales State Government. Authors express their appreciation to Judy Lambert, DWM program Coordinator; Andrew Petroeschevsky, National Aquatic Weeds Coordinator, NSW Department of Primary Industries, Grafton Agricultural Research & Advisory Station; Luke Joseph, Farm & Dam Control Pty Ltd: and SunWater for their invaluable advice. This project would not be possible without the dedicated work of Mitch Bryson, Calvin Hung, Alistair Reid, Nick Lawrence and Zhe Xu, and the support of ACFR's Aerospace Group members, particularly without the state-of-the-art engineering support from the team of Jeremy Randle, Steve Keep, and Muhammad Esa Attia.

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Australian Centre for Field Robotics (ACFR), School of Aerospace, Mechanical & Mechatronic Engineering (AMME)The University of SydneySydneyAustralia

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