Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria

PLoS ONE

Volumn 10, Issue 3, 2015, Pages

  Baranowski, Piotr ^a   Jedryczka, Malgorzata ^b   Mazurek, Wojciech ^a   Babula Skowronska, Danuta ^b   Siedliska, Anna ^a   Kaczmarek, Joanna ^b  

^a INSTITUTE OF AGROPHYSICS   (Poland)

^b INSTITUTE OF PLANT GENETICS   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ALTERNARIA; ALTERNARIA ALTERNATA; ALTERNARIA BRASSICAE; ALTERNARIA BRASSICICOLA; ALTERNARIA DAUCI; ARTICLE; BIOTIC STRESS; CONTROLLED STUDY; DISEASE SEVERITY; IMAGE ANALYSIS; IMAGING SYSTEM; INCIDENCE; INOCULATION; NEAR INFRARED IMAGING SYSTEM; NERVE CELL NETWORK; NONHUMAN; PLANT LEAF; RAPESEED; SHORT WAVELENGTH INFRARED; SPECTROSCOPY; TEMPERATURE; VISIBLE AND NEAR INFRARED; ALGORITHM; MICROBIOLOGY; PATHOGENICITY; PHYSIOLOGY;

ALTERNARIA; ALTERNARIA ALTERNATA; ALTERNARIA BRASSICAE; ALTERNARIA BRASSICICOLA; ALTERNARIA DAUCI; BRASSICA NAPUS;

ALGORITHMS; ALTERNARIA; BRASSICA NAPUS;

EID: 84926623097     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0122913     Document Type: Article

References (51)

1. Bravo C, Moshou D, West J, McCartney A, Ramon H. Early disease detection in wheat fields using spectral reflectance. Biosyst Eng 2003; 84(2): 137-145.
2. Glazebrook J. Contrasting mechanisms of defence against biotrophic and necrotrophic pathogens. Annu Rev Phytopathol 2005; 43: 205-227. PMID: 16078883
3. Knogge W. Fungal infection of plants. Plant Cell 8 (10): 1711-1722. PMID: 12239359
4. Mitchell MT (1998) Machine Learning. Singapore: McGraw Hill. 352 p;1996.
5. Chaerle L, Hagenbeek D, De Bruyne E, Valcke R, Van Der Straeten D. Thermal and chlorophyll-fluorescence imaging distinguish plant-pathogen interactions at an early stage. Plant Cell Physiol 2004; 45: 887-896. PMID: 15295072
6. Jones HG. Application of thermal imaging and infrared sensing in plant physiology and ecophysiology. Adv Bot Res 2004; 41: 107-163.
7. Chaerle L, Van Der Straeten D. Imaging techniques and the early detection of plant stress. Trends Plant Sci 2000; 5: 495-501. PMID: 11077259
8. Anderson P, Cunningham A, Patel N, Morales F, Epstein P, et al. Emerging infectious diseases of plants: pathogen pollution, climate change, and agrotechnology drivers. Trends Ecol Evol 2004; 19: 535-544. PMID: 16701319
9. Harvell CD, Mitchell CE, Ward JR, Altizer S, Dobson AP, et al. Climate warming and disease risks for terrestrial and marine biota. Science 2002; 296: 2158-2162. PMID: 12077394
10. Carroll WM, Glaser AJ, Hellmich LR, Hunt ET, Sappington W, et al. Use of spectral vegetation indices derived from airborne hyperspectral imagery for detection of European corn borer infestation in Iowa corn plots. J Econ Entomol 2008; 101(5): 1614-1623. PMID: 18950044
11. Mahlein AK, Steiner U, Dehne HW, Oerke EC. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precis Agric 2010; 11(4): 413-431.
12. Naidu AR, Perry ME, Pierce JF, Mekuria T. The potential of spectral reflectance technique for the detection of grapevine leaf-roll associated virus-3 in two red-berried wine grape cultivars. Comput Electron Agr 2009; 66(1): 38-45.
13. Carter GA, Knapp AK. Leaf optical properties in higher plants: Linking spectral characteristics to stress and chlorophyll concentration. Am J Bot 2001; 88: 677-684. PMID: 11302854
14. Gitelson AA, Merzlyak NM, Chivkunova BO. Optical properties and non-destructive estimation of anthocyanin content in plant leaves. Photochem Photobiol 2001; 74(1): 38-45. PMID: 11460535
15. Larsolle A, Muhammed HH. Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density. Precis Agric 2007; 8(1): 37-47.
16. Steddom K, Bredehoeft WM, Khan M, Rush MC. Comparison of visual and multispectral radiometric disease evaluations of Cercospora leaf spot of sugar beet. Plant Dis 2005; 89(2): 153-158.
17. Steddom K, Heidel G, Jones D, Rush MC. Remote detection of rhizomania in sugar beets. Phytopathology 2003; 93(6): 720-726. doi: 10.1094/PHYTO.2003.93.6.720 PMID: 18943059
18. Delalieux S, Somers B, Verstraeten WW, Van Aardt ANJ, KeulemansW, et al. Hyperspectral indices to diagnose leaf biotic stress of apple plants considering leaf phenology. Int J Remote Sens 2009; 30(8): 1887-1912.
19. Penuelas J, Baret F, Filella I. Semiempirical indexes to assess carotenoids/chlorophyll-a ratio from leaf spectral reflectance. Photosynthetica 1995; 31(2): 221-230.
20. Wang X, Zhang M, Zhu J, Geng S. Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural networks (ANN). Int J Remote Sens 2008; 29(6): 1693-1706.
21. Wolf FJP, Verreet AJ. An integrated pest management system in Germany for the control of fungal leaf diseases in sugar beet: The IPM sugar beet model. Plant Dis 2002; 86(4): 336-344.
22. Delalieux S, Van Aardt J, KeulemansW, Schrevens E, Coppin P. Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: Non-parametric statistical approaches and physiological implications. Eur J Agron 2007; 27(1): 130-143.
23. Graeff S, Link J, Claupein W. Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements. Cent Eur J Biol 2006; 1(2): 275-288.
24. Zhang M, Liu X, O'Neill M. Spectral discrimination of Phytophthora infestans infection on tomatoes based on principal component and cluster analyses. Int J Remote Sens 2002; 23(6): 1095-1107.
25. Wu D, Feng L, Zhang C, He Y. Early detection of Botrytis cinerea on eggplant leaves based on visible and near-infrared spectroscopy. T ASABE 2008; 51 (3):1133-1139.
26. Gutiérrez PA, López-Granados F, Pena-Barragán JM, Jurado-Expósito M, Gómez- Casero MT, et al. Mapping sunflower yield as affected by Ridolfia segetum patches and elevation by applying evolutionary product unit neural networks to remote-sensed data. Comput Electron Agr 2008; 60 (2): 122-132.
27. Baranowski P, Mazurek W, Pastuszka-Wo niak J. Supervised classification of bruised apples with respect to the time after bruising on the basis of hyperspectral imaging data. Postharvest Biol Tec 2013; 86: 249-258.
28. Hall MA. Correlation-based feature subset selection for machine learning. Ph.D. Thesis, University of Waikato;1998.
29. Karimi Y, Prasher OS, Patel RM, Kim MR, Kim HS. Application of support vector machine technology for weed and nitrogen stress detection in corn. Comput Electron Agr 2006:51 (1-2:) : 99-109.
30. Siedliska A, Baranowski P, Mazurek W. Classification models of bruise and cultivar detection on the basis of hyperspectral imaging data. Comput Electron Agr 2014; 106: 66-74.
31. Hatfield LJ, Gitelson AA, Schepers SJ, Walthall LC. Application of spectral remote sensing for agronomic decisions. Agron J 2008; 100(3): 117-131.
32. Baranowski P, Mazurek W, Jêdryczka M, Babula-Skowroñska D. Temperature changes of oilseed rape (Brassica napus) leaves infected by fungi of Alternaria sp. Oilseed Crops 2009;21-33.
33. Berger S, Papadopoulos M, Schreiber U, Kaiser W, Roitsch T. Complex regulation of gene expression, photosynthesis, and sugar levels by pathogen infection in tomato. Physiol Plantarum 2004; 122: 419-428.
34. Boccara M, Boue C, Garmier M, De Paepe R, Boccara A-C. Infrared thermography revealed a role for mitochondria in pre-symptomatic cooling during harpin-induced hypersensitive response. Plant J 2001; 28: 663-670. PMID: 11851912
35. Chou H-M, Bundock N, Rolfe SA, Scholes JD. Infection of Arabidopsis thaliana leaves with Albugo candida (white blister rust) causes a reprogramming of host metabolism. Mol Plant Pathol 2000; 1: 99-113. doi: 10.1046/j.1364-3703.2000.00013.x PMID: 20572957
36. Lindenthal M, Steiner U, Dehne HW, Oerke EC. Effect of downy mildew development on transpiration of cucumber leaves visualised by digital infrared thermography. Phytopathology 2005; 95: 233-240. doi: 10.1094/PHYTO-95-0233 PMID: 18943115
37. Camargo A, Smith J. Image pattern classification for the identification of disease-causing agents in plants. Comput Electron Agr 2009; 66(2): 121-125.
38. Oerke EC, Steiner U, Dehne HW, Lindenthal M.Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions. J Exp Bot 2006; 57: 2121-2132. PMID: 16714311
39. Belin E, Rousseau D, Léchappé J, Langlois-Meurinne M, Dürr C. Rate distortion tradeoff to optimise high-throughput phenotyping systems: Application to X-ray images of seeds. Comput Electron Agr 2011; 77: 188-194.
40. Oerke EC, Fröhling P, Steiner U. Thermographic assessment of scab disease on apple leaves. Precis Agric 2011; 12: 699-715.
41. Anastasia L, Lagopodi AL, Tziros GY.Formation of chlamydospores and microsclerotia in Alternaria dauci. Eur J Plant Pathol 2010; 128: 311-316.
42. Logrieco A, Moretti A, Solfrizzo M. Alternaria toxins and plant diseases: An overview of origin, occurrence, and risks. World Mycotoxin 2009;J2: 129-140.
43. Otani H, Kohmoto K. Host-specific toxins on Alternaria species. In: Chelkowski J, Visconti A, editors. Alternaria: Biology, plant diseases, and metabolites. Amsterdam: Elsevier Science Publishers. pp. 573;1992.
44. Allen SJ, Brown JF, Kochman JK. The infection process, sporulation, and survival of Alternaria helianthi on sunflower. Ann Appl Biol 1983; 102: 413-419.
45. Saharan YM, Kadin AK. Analysis of components of horizontal resistance in rape seed and mustard cultivars against Alternaria brassicae. Indian Phytopath 1983; 36 (3): 503-507.
46. Witten IH, Frank E. Data mining: Practical machine learning tools and techniques, 2nd ed. San Francisco: Morgan Kaufmann Publishers/Elsevier. 525 p; 2005.
47. Hellebrand HJ, Herppich WB, Beuche H, Dammer K- H, Linke M, et al. Investigations of plant infections by thermal vision and NIR imaging. Int Agrophys 2006; 20 (1): 1-10.
48. Bock CH, Poole GH, Parker PE, Gottwald TR. Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Crit Rev Plant Sci 2010; 29: 59-107.
49. Bauriegel E, Giebel A, Geyer M, Schmidt U, Herppich WB. Early detection of Fusarium infection in wheat using hyperspectral imaging. Comput Electron Agr 2011; 75 (2): 304-312.
50. Magan N, Cayley GR, Lacey J. Effects of water activity and temperature on mycotoxin production by Alternaria alternata in culture and on wheat grain. Appl Environ Microbiol 1984; 47: 1113-1117. PMID: 6540067
51. Mahlein AK, Oerke EC, Steiner U, Dehne HW. Recent advances in sensing plant diseases for precision crop protection. Eur J Plant Pathol 2012; 133: 197-209.