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Drones 2019, 3(1), 25; https://doi.org/10.3390/drones3010025

Multispectral, Aerial Disease Detection for Myrtle Rust (Austropuccinia psidii) on a Lemon Myrtle Plantation

1
Biodiversity, Ecology and Evolution of Plants, Institute of Plant Science and Microbiology, University of Hamburg, 22609 Hamburg, Germany
2
Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia
3
Joint Remote Sensing Research Program, School of Earth and Environmental Sciences, University of Queensland, Brisbane, QLD 4072, Australia
4
Forest Science, Department of Primary Industries - Forestry, Parramatta, NSW 2150, Australia
5
Occupational Hygiene, Environment and Chemistry Centre, Simtars, Redbank, QLD 4301, Australia
*
Author to whom correspondence should be addressed.
Received: 1 February 2019 / Revised: 26 February 2019 / Accepted: 1 March 2019 / Published: 7 March 2019
(This article belongs to the Special Issue UAV/Drones for Agriculture and Forestry)
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Abstract

Disease management in agriculture often assumes that pathogens are spread homogeneously across crops. In practice, pathogens can manifest in patches. Currently, disease detection is predominantly carried out by human assessors, which can be slow and expensive. A remote sensing approach holds promise. Current satellite sensors are not suitable to spatially resolve individual plants or lack temporal resolution to monitor pathogenesis. Here, we used multispectral imaging and unmanned aerial systems (UAS) to explore whether myrtle rust (Austropuccinia psidii) could be detected on a lemon myrtle (Backhousia citriodora) plantation. Multispectral aerial imagery was collected from fungicide treated and untreated tree canopies, the fungicide being used to control myrtle rust. Spectral vegetation indices and single spectral bands were used to train a random forest classifier. Treated and untreated trees could be classified with high accuracy (95%). Important predictors for the classifier were the near-infrared (NIR) and red edge (RE) spectral band. Taking some limitations into account, that are discussedherein, our work suggests potential for mapping myrtle rust-related symptoms from aerial multispectral images. Similar studies could focus on pinpointing disease hotspots to adjust management strategies and to feed epidemiological models. View Full-Text
Keywords: disease detection; drones; plant disease; precision agriculture; random forest; remote sensing; rust fungus; shadow; UAS disease detection; drones; plant disease; precision agriculture; random forest; remote sensing; rust fungus; shadow; UAS
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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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Heim, R.H.; Wright, I.J.; Scarth, P.; Carnegie, A.J.; Taylor, D.; Oldeland, J. Multispectral, Aerial Disease Detection for Myrtle Rust (Austropuccinia psidii) on a Lemon Myrtle Plantation. Drones 2019, 3, 25.

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