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Sensors 2018, 18(1), 260; https://doi.org/10.3390/s18010260

A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data

1
Institute for Future Environments, Robotics and Autonomous Systems, Queensland University of Technology, 2 George St, Brisbane, QLD 4000, Australia
2
Agriculture Victoria Research, Victorian Department of Economic Development, Jobs, Transport and Resources, Rutherglen, VIC 3083, Australia
3
Plant Biosecurity Cooperative Research Centre, Bruce, ACT 2817, Australia
4
Agriculture Victoria Research, Victorian Department of Economic Development, Jobs, Transport and Resources AgriBio Centre, 5 Ring Road, Bundoora, VIC 3083, Australia
Current address: Sugar Research Australia, Meringa, QLD 4865, Australia.
*
Author to whom correspondence should be addressed.
Received: 30 November 2017 / Revised: 10 January 2018 / Accepted: 14 January 2018 / Published: 17 January 2018
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Abstract

Recent advances in remote sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have enabled the rapid and ongoing development of monitoring tools for crop management and the detection/surveillance of insect pests. This paper describes a (UAV) remote sensing-based methodology to increase the efficiency of existing surveillance practices (human inspectors and insect traps) for detecting pest infestations (e.g., grape phylloxera in vineyards). The methodology uses a UAV integrated with advanced digital hyperspectral, multispectral, and RGB sensors. We implemented the methodology for the development of a predictive model for phylloxera detection. In this method, we explore the combination of airborne RGB, multispectral, and hyperspectral imagery with ground-based data at two separate time periods and under different levels of phylloxera infestation. We describe the technology used—the sensors, the UAV, and the flight operations—the processing workflow of the datasets from each imagery type, and the methods for combining multiple airborne with ground-based datasets. Finally, we present relevant results of correlation between the different processed datasets. The objective of this research is to develop a novel methodology for collecting, processing, analising and integrating multispectral, hyperspectral, ground and spatial data to remote sense different variables in different applications, such as, in this case, plant pest surveillance. The development of such methodology would provide researchers, agronomists, and UAV practitioners reliable data collection protocols and methods to achieve faster processing techniques and integrate multiple sources of data in diverse remote sensing applications. View Full-Text
Keywords: remote sensing; unmanned aerial vehicle; phylloxera; multispectral; hyperspectral; RGB; digital elevation model; digital vigour assessment remote sensing; unmanned aerial vehicle; phylloxera; multispectral; hyperspectral; RGB; digital elevation model; digital vigour assessment
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Vanegas, F.; Bratanov, D.; Powell, K.; Weiss, J.; Gonzalez, F. A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data. Sensors 2018, 18, 260.

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