Detecting myrtle rust (Austropuccinia psidii) on lemon myrtle trees using spectral signatures and machine learning |
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| Authors: | R. H. J. Heim I. J. Wright H.‐C. Chang A. J. Carnegie G. S. Pegg E. K. Lancaster D. S. Falster J. Oldeland |
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| Affiliation: | 1. Department of Biological Sciences, Macquarie University, Sydney, NSW, Australia;2. Biodiversity, Ecology and Evolution of Plants, Biocentre Klein Flottbek and Botanical Garden, University of Hamburg, Hamburg, Germany;3. Department of Environmental Sciences, Macquarie University, Sydney, NSW, Australia;4. NSW Department of Primary Industries, NSW Forest Science, Parramatta, NSW, Australia;5. Department of Agriculture and Fisheries, Horticulture and Forestry Science, Brisbane, Qld, Australia;6. Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Qld, Australia;7. School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW, Australia |
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| Abstract: | Hundreds of species in one of Australia's dominant plant families, the Myrtaceae, are at risk from the invasive pathogenic fungus Austropuccinia psidii. Since its arrival in Australia in 2010, native plant communities have been severely affected, with highly susceptible species likely to become extinct from recurring infections. While severe impact on Australian native and plantation forestry has been predicted, the lemon myrtle industry is already under threat. Commercial cultivars of lemon myrtle (Backhousia citriodora) are highly susceptible to A. psidii. Detecting and monitoring disease outbreaks is currently only possible by eye, which is costly and subject to human bias. This study aims at developing a proof‐of‐concept for automated, non‐biased classification of healthy (naïve), fungicide‐treated and diseased lemon myrtle trees by means of their spectral reflectance signatures. From a lemon myrtle plantation, spectral signatures of fungicide‐treated and untreated leaves were collected using a portable field spectrometer. A third class of spectra, from naïve lemon myrtle leaves that had not been exposed to A. psidii, was collected from a botanical garden. Reflectance spectra in their primary form and their first‐order derivatives were used to train a random forest classifier resulting in an overall accuracy of 78% (kappa = 0.68) for primary spectra and 95% (kappa = 0.92) for first‐order derivative‐transformed spectra. Thus, an optical sensor‐based discrimination, using spectral reflectance signatures of this as yet uninvestigated pathosystem, seems technically feasible. This study provides a foundation for the development of automated, sensor‐based detection and monitoring systems for myrtle rust. |
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| Keywords: | derivative spectrometry field spectroscopy plant pathogens precision agriculture random forest rust fungi |
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