Detecting myrtle rust (Austropuccinia psidii) on lemon myrtle trees using spectral signatures and machine learning

Plant Pathology

Volumn 67, Issue 5, 2018, Pages 1114-1121

  Heim, R H J ^a,b   Wright, I J ^a   Chang, H C ^a   Carnegie, A J ^c   Pegg, G S ^d   Lancaster, E K ^e   Falster, D S ^f   Oldeland, J ^b  

^a MACQUARIE UNIVERSITY   (Australia)

^b UNIVERSITY OF HAMBURG   (Germany)

^c NSW DEPARTMENT OF PRIMARY INDUSTRIES   (Australia)

^d DEPARTMENT OF AGRICULTURE AND FISHERIES   (Australia)

^e UNIVERSITY OF QUEENSLAND   (Australia)

^f UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

derivative spectrometry; field spectroscopy; plant pathogens; precision agriculture; random forest; rust fungi

Indexed keywords

COMMERCIAL ACTIVITY; CULTIVAR; DETECTION METHOD; EVERGREEN TREE; INVASIVE SPECIES; MACHINE LEARNING; NATIVE SPECIES; PATHOGEN; PLANT COMMUNITY; PLANTATION FORESTRY; POPULATION OUTBREAK; PRECISION AGRICULTURE; RUST DISEASE; SPECTRAL ANALYSIS; SPECTRAL REFLECTANCE; SPECTROMETRY;

AUSTRALIA;

BACKHOUSIA CITRIODORA; CITRUS LIMON; FUNGI; MYRTACEAE; UREDINALES;

EID: 85041912897     PISSN: 00320862     EISSN: 13653059     Source Type: Journal    
DOI: 10.1111/ppa.12830     Document Type: Article

References (45)

1. Alfenas AC, Zauza EAV, Assis TF, 2003. First record of Puccinia psidii on Eucalyptus globulus and E. viminalis in Brazil. Australasian Plant Pathology 32, 325–6.
2. Australian Government Bureau of Meteorology, 2017. Climate data online. [http://www.bom.gov.au/climate/data/index.shtml?bookmark=200]. Accessed 17 August 2017.
3. Berthon K, Esperon-Rodrigues M, Beaumont L, Carnegie AJ, Leishman L, 2018. Assessment and prioritisation of plant species at risk from myrtle rust (Austropuccinia psidii) under current and future climates in Australia. Biological Conservation 218, 247–53.
4. Bhattacharya S, 2017. Deadly new wheat disease threatens Europe's crops. Nature 542, 145–6.
5. Bravo C, Moshou D, West J, McCartney A, Ramon H, 2003. Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering 84, 137–45.
6. Breiman L, 2001. Random forests. Machine Learning 45, 5–32.
7. Carnegie AJ, Lidbetter JR, Walker J et al., 2010. Uredo rangelii, a taxon in the guava rust complex, newly recorded on Myrtaceae in Australia. Australasian Plant Pathology 39, 463–6.
8. Carnegie AJ, Kathuria A, Pegg GS, Entwistle P, Nagel M, Giblin FR, 2016. Impact of the invasive rust Puccinia psidii (myrtle rust) on native Myrtaceae in natural ecosystems in Australia. Biological Invasions 18, 127–44.
9. Cohen J, 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20, 37–46.
10. Coutinho TA, Wingfield MJ, Alfenas AC, Crous PW, 1998. Eucalyptus rust: a disease with the potential for serious international implications. Plant Disease 82, 819–25.
11. Delalieux S, van Aardt J, Keulemans W, Coppin P, 2007. Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: non-parametric statistical approaches and physiological implications. European Journal of Agronomy 27, 130–43.
12. Demetriades-Shah TH, Steven MD, Clark JA, 1990. High resolution derivative spectra in remote sensing. Remote Sensing of Environment 33, 55–64.
13. Doran J, Lea D, Bush D, 2012. Assessing Myrtle Rust in a Lemon Myrtle Provenance Trial. Rural Industries Research and Development Corporation, Barton, Australia.
14. Febrero-Bande M, Oviedo de la Fuente M, 2012. Statistical computing in functional data analysis: The R package fda.usc. Journal of Statistical Software 51, 1–28.
15. Genuer R, Poggi J-M, Tuleau-Malot C, 2015. vsurf: An R package for variable selection using random forests. The R Journal 7, 19–33.
16. Ghiyamat A, Shafri HZM, Amouzad Mahdiraji G, Shariff ARM, Mansor S, 2013. Hyperspectral discrimination of tree species with different classifications using single- and multiple-endmember. International Journal of Applied Earth Observation and Geoinformation 23, 177–91.
17. Giblin F, 2013. Myrtle Rust Report: New Caledonia. University of the Sunshine Coast, Maroochydore.
18. Hastie T, Tibshirani R, Friedman J, 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edn. Berlin, Germany: Springer Science & Business Media.
19. Helfer S, 2014. Rust fungi and global change. New Phytologist 201, 770–80.
20. Immitzer M, Atzberger C, Koukal T, 2012. Tree species classification with random forest using very high spatial resolution 8-band Worldview-2 satellite data. Remote Sensing 4, 2661–93.
21. Jensen JR, 2009. Remote Sensing of the Environment: An Earth Resource Perspective. 2nd edn. New Delhi, India: Pearson Education India.
22. Jones HG, Vaughan RA, 2010. Remote Sensing of Vegetation: Principles, Techniques, and Applications. Oxford, UK: Oxford University Press.
23. Kuhn M, Johnson K, 2013. Applied Predictive Modeling. New York, NY, USA: Springer.
24. Kuhn M, Wing J, Weston S et al., 2017. caret: Classification and regression training. R package version 6.0-76. [https://CRAN.R-project.org/package=caret.]. Accessed 9 January 2018.
25. Landis JR, Koch GG, 1977. The measurement of observer agreement for categorical data. Biometrics 33, 159–74.
26. Loope L, 2010. A Summary of Information on the Rust Puccinia psidii Winter (Guava Rust) with Emphasis on Means to Prevent Introduction of Additional Strains to Hawaii. Open-File Report 2010–1082. Reston, Virginia: US Geological Survey.
27. Machado P, Alfenas AC, Alfenas RF, Mohammed CL, Glen M, 2015. Microsatellite analysis indicates that Puccinia psidii in Australia is mutating but not recombining. Australasian Plant Pathology 44, 455–62.
28. Mahlein AK, 2016. Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease 100, 241–51.
29. Mahlein AK, Steiner U, Dehne H-W, Oerke E-C, 2010. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agriculture 11, 413–31.
30. Mahlein AK, Rumpf T, Welke P et al., 2013. Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment 128, 21–30.
31. Makinson B, 2014. Myrtle rust – what's happening? Australasian Plant Conservation 23, 13–5.
32. McTaggart AR, Roux J, Granados GM et al., 2016. Rust (Puccinia psidii) recorded in Indonesia poses a threat to forests and forestry in South-East Asia. Australasian Plant Pathology 45, 83–9.
33. Morin L, Talbot MJ, Glen M, 2014. Quest to elucidate the life cycle of Puccinia psidii sensu lato. Fungal Biology 118, 253–63.
34. Mutanga O, Skidmore A, Prins HH, 2004. Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sensing of Environment 89, 393–408.
35. NSW Scientific Committee, 2011. Introduction and Establishment of Exotic Rust Fungi of the Order Pucciniales Pathogenic on Plants of the Family Myrtaceae – Key Threatening Process Listing. Sydney, Australia: NSW Scientific Committee.
36. Pegg GS, Brawner JT, Lee DJ, 2014. Screening Corymbia populations for resistance to Puccinia psidii. Plant Pathology 63, 425–36.
37. du Plessis E, McTaggart AR, Granados GM et al., 2017. First report of myrtle rust caused by Austropuccinia psidii on Rhodomyrtus tomentosa (Myrtaceae) from Singapore. Plant Disease 101, 1676.
38. Potts BM, Sandhu KS, Wardlaw T et al., 2016. Evolutionary history shapes the susceptibility of an island tree flora to an exotic pathogen. Forest Ecology and Management 368, 183–93.
39. R Core Team, 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
40. Roux J, Greyling I, Coutinho TA, Verleur M, Wingfield MJ, 2013. The myrtle rust pathogen, Puccinia psidii, discovered in Africa. IMA Fungus 4, 155–9.
41. Rural Industries Research and Development Corporation, 2012. Lemon Myrtle. RIRDC Publication No. 12/066. Canberra, Australia: Australian Native Food Industry Stocktake, RIRDC, 5–7.
42. Soewarto J, Carriconde F, Hugot N, Bocs S, Hamelin C, Maggia L, 2017. Impact of Austropuccinia psidii in New Caledonia, a biodiversity hotspot. Forest Pathology. https://doi.org/10.1111/efp.12402.
43. Stevens A, Ramirez-Lopez L, 2014. An introduction to the prospectr package. R Package Vignette, Report No. 3, R Package version 0.1. [https://cran.r-project.org/web/packages/prospectr/vignettes/prospectr-intro.pdf.] Accessed 9 January 2018.
44. Story M, Congalton R, 1986. Accuracy assessment – a user's perspective. Photogrammetric Engineering and Remote Sensing 52, 397–9.
45. Thenkabail PS, Lyon GJ, Huete A, 2011. Hyperspectral Remote Sensing of Vegetation. Boca Raton, FL, USA: CRC Press.