Detecting myrtle rust (Austropuccinia psidii ) on lemon myrtle trees using spectral signatures and machine learning
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Title
Detecting myrtle rust (Austropuccinia psidii
) on lemon myrtle trees using spectral signatures and machine learning
Authors
Keywords
-
Journal
PLANT PATHOLOGY
Volume 67, Issue 5, Pages 1114-1121
Publisher
Wiley
Online
2018-01-17
DOI
10.1111/ppa.12830
References
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Related references
Note: Only part of the references are listed.- Assessment and prioritisation of plant species at risk from myrtle rust ( Austropuccinia psidii ) under current and future climates in Australia
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- (2016) Anne-Katrin Mahlein PLANT DISEASE
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- ScreeningCorymbiapopulations for resistance toPuccinia psidii
- (2013) G. S. Pegg et al. PLANT PATHOLOGY
- The Myrtle rust pathogen, puccinia psidii, discovered in Africa
- (2013) Jolanda Roux et al. IMA Fungus
- Development of spectral indices for detecting and identifying plant diseases
- (2012) A.-K. Mahlein et al. REMOTE SENSING OF ENVIRONMENT
- Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data
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- (2010) A. J. Carnegie et al. AUSTRALASIAN PLANT PATHOLOGY
- Spectral signatures of sugar beet leaves for the detection and differentiation of diseases
- (2010) A.-K. Mahlein et al. PRECISION AGRICULTURE
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