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European Journal of Plant Pathology

, Volume 152, Issue 4, pp 869–884 | Cite as

Hyperspectral quantification of wheat resistance to Fusarium head blight: comparison of two Fusarium species

  • E. Alisaac
  • J. Behmann
  • M. T. Kuska
  • H.-W. Dehne
  • A.-K. Mahlein
Article

Abstract

Interactions of Fusarium species with different wheat varieties differ in their temporal dynamics and symptom appearance. Reliable and objective approaches for monitoring processes during infection are demanded for plant phenotyping and disease rating. This study presents an automated method to phenotype wheat varieties to Fusarium head blight (FHB) using hyperspectral sensors. In time-series experiments, the optical properties of spikes infected with F. graminearum or F. culmorum were recorded. Two hyperspectral cameras, in visible and near-infrared (VIS-NIR, 400–1000 nm) and shortwave-infrared (SWIR, 1000–2500 nm) captured the most relevant bands for pigments, cell structure, water and further compounds. Correlations between disease severity (DS), spike weight, spectral bands and vegetation indices were investigated. Following, the detectability of infections was assessed by Support Vector Machine (SVM) classifier. A variety ranking based on AUDPC was performed and compared to a fully-automated approach using Non-metric Multi-Dimensional Scaling (NMDS). High correlation was found between the spectral signature and DS in 430–525 nm, 560–710 nm and 1115–2500 nm. All indices from the VIS-NIR showed high correlation with DS and, for the first time, this was also confirmed for three indices from the SWIR: NDNI, CAI and MSI. Using SVM, differentiation between healthy and infected spikes was possible (acc. > 0.76). Furthermore, the possibility to differentiate between F. graminearum and F. culmorum infected spikes has been verified. The NMDS approach was able to reproduce accurately the variety ranking and outlines the potential of hyperspectral imaging to phenotype the variety susceptibility for improved breeding processes.

Keywords

Wheat F. graminearum F. culmorum Phenotyping Hyperspectral imaging Spectral signature Spectral vegetation indices (SVIs) Support vector machine (SVM) AUDPC Non-metric multidimensional scaling (NMDS) 

Notes

Acknowledgements

This study was funded by the German Federal Ministry of Education and Research (BMBF) within the scope of the competitive grants program “Networks of excellence in agricultural and nutrition research - CROP.SENSe.net” (Funding code: 0315529), Junior Research Group “Hyperspectral phenotyping of resistance reactions of barley” and due to financial support of the Catholic Academic Exchange Service (KAAD).

Supplementary material

10658_2018_1505_Fig8_ESM.gif (44 kb)
Fig. S1

Progress of spectral signature of control spikes 4, 21 and 21 dai respectively. (GIF 43 kb)

10658_2018_1505_MOESM1_ESM.tif (278 kb)
High Resolution Image (TIF 277 kb)
10658_2018_1505_MOESM2_ESM.docx (14 kb)
Table S1 (DOCX 14 kb)

References

  1. Al Masri, A., Hau, B., Dehne, H.-W., Mahlein, A.-K., & Oerke, E.-C. (2017). Impact of primary infection site of Fusarium species on head blight development in wheat ears evaluated by IR-thermography. European Journal of Plant Pathology, 147, 855–868.CrossRefGoogle Scholar
  2. Alkadri, D., Rubert, J., Prodi, A., Pisi, A., Manes, J., & Soler, C. (2014). Natural co-occurrence of mycotoxins in wheat grains from Italy and Syria. Food Chemistry, 157, 111–118.CrossRefGoogle Scholar
  3. Aoki, T., O’Donnell, K., & Geiser, D. M. (2014). Systematics of key phytopathogenic Fusarium species: Current status and future challenges. Journal of General Plant Pathology, 80, 189–201.CrossRefGoogle Scholar
  4. Bauriegel, E., Giebel, A., Geyer, M., Schmidt, U., & Herppich, W. B. (2011). Early detection of Fusarium infection in wheat using hyper-spectral imaging. Computer and Electronics in Agriculture, 75, 304–312.CrossRefGoogle Scholar
  5. Behmann, J., Mahlein, A.-K., Rumpf, T., Römer, C., & Plümer, L. (2015). A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agriculture, 16, 239–260.CrossRefGoogle Scholar
  6. Behmann, J., Acebron, K., Emin, D., Bennertz, S., Matsubara, S., Thomas, S., Bohnenkamp, D., Kuska, M. T., Jussila, J., Salo, H., Mahlein, A. K., & Rascher, U. (2018). Specim IQ: Evaluation of a new, miniaturized handheld hyperspectral camera and its application for plant phenotyping and disease detection. Sensors, 18, 441.CrossRefGoogle Scholar
  7. Beukes, I., Rose, L. J., van Coller, G. J., & Viljoen, A. (2018). Disease development and mycotoxin production by the Fusarium graminearum species complex associated with south African maize and wheat. European Journal of Plant Pathology, 150, 893–910.CrossRefGoogle Scholar
  8. Birzele, B., Meier, A., Hindorf, H., Krämer, J., & Dehne, H.-W. (2002). Epidemiology of Fusarium infection and Deoxynivalenol content in winter wheat in the Rhineland, Germany. European Journal of Plant Pathology, 108, 667–673.CrossRefGoogle Scholar
  9. Blackburn, G. A. (1998a). Quantifying chlorophylls and carotenoids at leaf and canopy scale: An evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66, 273–285.CrossRefGoogle Scholar
  10. Blackburn, G. A. (1998b). Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. International Journal of Remote Sensing, 19, 657–675.CrossRefGoogle Scholar
  11. Borg, I., & Groenen, P. (2005). Modern multidimensional scaling: Theory and applications (2nd ed.). New York: Springer-Verlag.Google Scholar
  12. Brown, N. A., Urban, M., van deMeene, A. M. L., & Hammond-Kosack, K. E. (2010). The infection biology of Fusarium graminearum: Defining the pathways of spikelet to spikelet colonisation in wheat ears. Fungal Biology, 114, 555–571.CrossRefGoogle Scholar
  13. Buerstmayr, H., Ban, T., & Anderson, J. A. (2009). QTL mapping and marker-assisted selection for Fusarium head blight resistance in wheat; a review. Plant Breeding, 128, 1–26.CrossRefGoogle Scholar
  14. Bushnell, W. R., Hazen, B. E., & Pritsch, C. (2003). Histology and physiology of Fusarium head blight. In K. J. Leonard & W. R. Bushnell (Eds.), Fusarium head blight of wheat and barley (pp. 44–83). St. Paul, Minnesota: APS Press.Google Scholar
  15. Chetouhi, C., Bonhomme, L., Lecomte, P., Cambon, F., Merlino, M., Biron, D. G., & Langin, T. (2015). A proteomics survey on wheat susceptibility to Fusarium head blight during grain development. European Journal of Plant Pathology, 141, 407–418.CrossRefGoogle Scholar
  16. Cortes, C., & Vapnik, N. V. (1995). Support-vector networks. Machine Learning, 20, 273–297.Google Scholar
  17. Daub, M. E., & Ehrenshaft, M. (2000). The photoactivated Cercospora toxin cercosporin: Contributions to plant disease and fundamental biology. Annual Review of Phytopathology, 38, 461–490.CrossRefGoogle Scholar
  18. Daughtry, C. S. T. (2001). Discriminating crop residues from soil by shortwave infrared reflectance. Agronomy Journal, 93, 125–131.CrossRefGoogle Scholar
  19. Delalieux, S., Somers, B., Verstraeten, W. W., van Aardt, J. A. N., Keulemans, W., & Coppin, P. (2009). Hyperspectral indices to diagnose leaf biotic stress of apple plants, considering leaf phenology. International Journal of Remote Sensing, 30, 1887–1912.CrossRefGoogle Scholar
  20. van der Lee, T., Zhang, H., van Diepeningen, A., & Waalwijk, C. (2015). Biogeography of Fusarium graminearum species complex and chemotypes: A review. Food Additives & Contaminants: Part A, 32, 453–460.CrossRefGoogle Scholar
  21. Descriptive List of Varieties, Bundessortenamt, Germany. (2017). Getreide, Mais, Öl- und Faserpflanzen, Leguminosen, Rüben, Zwischenfrüchte. In Bundessortenamt. Deutschland: Hannover https://www.bundessortenamt.de/internet30/index.php?id=41&tx_ttnews%5Btt_news%5D=308&cHash=bb2220e6c08a91dfd6a99e8fdf6575a3.Google Scholar
  22. Dweba, C. C., Figlan, S., Shimelis, H. A., Motaung, T. E., Sydenham, S., Mwadzingeni, L., & Tsilo, T. J. (2017). Fusarium head blight of wheat: Pathogenesis and control strategies. Crop Protection, 91, 114–122.CrossRefGoogle Scholar
  23. Fahlgren, N., Feldman, M., Gehan, M., Wilson, M. S., Shyu, C., Bryant, D. W., Hill, S. T., McEntee, C. J., Warnasooriya, S. N., Kumar, I., Ficor, T., Turnipseed, S., Gilbert, K. B., Brutnell, T. P., Carrington, J. C., Mockler, T. C., & Baxter, I. (2015). A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in Setaria. Molecular Plant, 8, 1–16.CrossRefGoogle Scholar
  24. Ferrigo, D., Alessandro Raiola, A., & Roberto, R. (2016). Fusarium toxins in cereals: Occurrence, legislation, factors promoting the appearance and their management. Molecules, 21, 627.CrossRefGoogle Scholar
  25. Furbank, R. T., & Tester, M. (2011). Phenomics - technologies to relieve the phenotyping bottleneck. Trends in Plant Science, 16, 635–644.CrossRefGoogle Scholar
  26. Gamon, J. A., Peñeulas, J., & Field, C. B. (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41, 35–44.CrossRefGoogle Scholar
  27. Gilbert, J., & Haber, S. (2013). Overview of some recent research developments in Fusarium head blight of wheat. Canadian Journal of Plant Pathology, 35, 149–174.CrossRefGoogle Scholar
  28. Ha, X., Koopmann, B., & von Tiedemann, A. (2016). Wheat blast and Fusarium head blight display contrasting interaction patterns on ears of wheat genotypes differing in resistance. Phytopathology, 106, 270–281.CrossRefGoogle Scholar
  29. Hunt, E., & Rock, B. (1989). Detection of changes in leaf water-content using near-infrared and middle-infrared reflectances. Remote Sensing of Environment, 30, 43–54.CrossRefGoogle Scholar
  30. Iori, A., Scala, V., Cesar, D., Pinzari, F., D’Egidio, M. G., Fanelli, C., Fabbri, A. A., Reverberi, M., & Serranti, S. (2015). Hyperspectral and molecular analysis of Stagonospora nodorum blotch disease in durum wheat. European Journal of Plant Pathology, 141, 689–702.CrossRefGoogle Scholar
  31. Johnson, D. D., Flakerud, G. K., Taylor, R. D., & Satyanarayana, V. (2003). Quantifying economic impacts of Fusarium head blight in wheat. In K. J. Leonard & W. R. Bushnell (Eds.), Fusarium head blight of wheat and barley (pp. 461–484). St. Paul, Minnesota: APS Press.Google Scholar
  32. Kreuzberger, M., Limsuwan, S., Eggert, K., Karlovsky, P., & Pawelzik, E. (2015). Impact of Fusarium spp. infection of bread wheat (Triticum aestivum L.) on composition and quality of flour in association with EU maximum level for deoxynivalenol. Journal of Applied Botany and Food Quality, 88, 177–185.Google Scholar
  33. Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29, 1–27.CrossRefGoogle Scholar
  34. Kuhnem, P. R., Del Ponte, E. M., Dong, Y., & Bergstrom, G. C. (2015). Fusarium graminearum isolates from wheat and maize in New York show similar range of aggressiveness and Toxigenicity in cross-species pathogenicity tests. Phytopathology, 105, 441–448.CrossRefGoogle Scholar
  35. Kuska, M. T., & Mahlein, A.-K. (2018). Aiming at decision making in plant disease protection and phenotyping by the use of optical sensors. European Journal of Plant Pathology.  https://doi.org/10.1007/s10658-018-1464-1.
  36. Kuska, M. T., Brugger, A., Thomas, S., Wahabzada, M., Kersting, K., Oerke, E. C., Steiner, U., & Mahlein, A. K. (2017). Spectral patterns reveal early resistance reactions of barley against Blumeria graminis f. Sp. hordei. Phytopathology, 107, 1388–1398.CrossRefGoogle Scholar
  37. Lancashire, P. D., Bleiholder, H., Van den Boom, T., Langeluddecke, P., Stauss, R., Weber, E., & Witzenberger, A. (1991). A uniform decimal code for growth stages of crops and weeds. Annals of Applied Biology, 119, 561–601.CrossRefGoogle Scholar
  38. Leucker, M., Mahlein, A.-K., Steiner, U., & Oerke, E.-C. (2016). Improvement of lesion phenotyping in Cercospora beticola-sugar beet interaction by hyperspectral imaging. Phytopathology, 106, 177–184.CrossRefGoogle Scholar
  39. Mahlein, A.-K. (2016). Present and future trends in plant disease detection. Plant Disease, 100, 241–251.CrossRefGoogle Scholar
  40. Mahlein, A.-K., 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, 413–431.Google Scholar
  41. Mahlein, A.-K., Steiner, U., Hillnhütter, C., Dehne, H.-W., & Oerke, E.-C. (2012). Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods, 8, 3.CrossRefGoogle Scholar
  42. McCormick, S. (2003). The role of DON in pathogenicity. In K. J. Leonard & W. R. Bushnell (Eds.), Fusarium head blight of wheat and barley (pp. 165–183). St. Paul, Minnesota: APS Press.Google Scholar
  43. McMullen, M., Bergstrom, G., De Wolf, E., Dill-Macky, R., Hershman, D., Shaner, G., & Van Sanford, D. (2012). A unified effort to fight an enemy of wheat and barley: Fusarium head blight. Plant Disease, 96, 1712–1728.CrossRefGoogle Scholar
  44. Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B., & Rakitin, V. Y. (1999). Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologica Plantarum, 106, 135–141.CrossRefGoogle Scholar
  45. Mesterházy, Á., Buerstmayr, H., Tóth, B., Lehoczki-Krsjak, Sz., Szabó-Hevér, Á. & Lemmens, M. (2007). An improved strategy for breeding FHB resistant wheat must include type I resistance. In Proceedings of the 5th Canadian workshop on Fusarium head blight, 27-30 November 2007, Delta Winnipeg (Canada), 51–66.Google Scholar
  46. Mesterházy, Á., Lehoczki-Krsjak, S., Varga, M., Szabó-Hevér, Á., Tóth, B., & Lemmens, M. (2015). Breeding for FHB resistance via Fusarium damaged kernels and Deoxynivalenol accumulation as well as inoculation methods in winter wheat. Agricultural Sciences, 6, 970–1002.CrossRefGoogle Scholar
  47. Moradi, G. M. (2008). Microbiological and molecular assessment of interactions among the major Fusarium head blight pathogens on wheat ear. In Bonn. Germany: University of Bonn. PhD thesis.Google Scholar
  48. Nagler, P. L., Inoue, Y., Glenn, E. P., Russ, A. L., & Daughtry, C. S. T. (2003). Cellulose absorption index (CAI) to quantify mixed soil–plant litter scenes. Remote Sensing of Environment, 87, 310–325.CrossRefGoogle Scholar
  49. O'Donnell, K., Ward, T. J., Geiser, D. M., Kistler, H. C., & Aoki, T. (2004). Genealogical concordance between the mating type locus and seven other nuclear genes supports formal recognition of nine phylogenetically distinct species within the Fusarium graminearum clade. Fungal Genetics and Biology, 41, 600–623.CrossRefGoogle Scholar
  50. Osborne, L. M., & Stein, J. M. (2007). Epidemiology of Fusarium head blight on small-grain cereals. International Journal of Food Microbiology, 119, 103–108.CrossRefGoogle Scholar
  51. Parry, D. W., Jenkinson, P., & McLeod, L. (1995). Fusarium ear blight (scab) in small grain cereals-a review. Plant Pathology, 44, 207–238.CrossRefGoogle Scholar
  52. Pasquali, M., Beyer, M., Logrieco, A., Audenaert, K., Balmas, V., Basler, R., Boutigny, A.-L., Chrpová, J., Czembor, E., Gagkaeva, T., González-Jaén, M. T., Hofgaard, I. S., Köycü, N. D., Hoffmann, L., Levic, J., Marin, P., Miedaner, T., Migheli, Q., Moretti, A., Müller, M. E. H., Munaut, F., Parikka, P., Pallez-Barthel, M., Piec, J., Scauflaire, J., Scherm, B., Stankovic, S., Thrane, U., Uhlig, S., Vanheule, A., Yli-Mattila, T., & Vogelgsang, S. (2016). A European database of Fusarium graminearum and F. culmorum trichothecene genotypes. Frontiers in Microbiology, 7, 406.CrossRefGoogle Scholar
  53. Peñuelas, J., Baret, F., & Filella, I. (1995). Semiempirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31, 221–230.Google Scholar
  54. Peñuelas, J., Pinol, R. O., Ogaya, R., & Filella, I. (1997). Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 18, 2869–2875.CrossRefGoogle Scholar
  55. Pestka, J. J. (2010). Deoxynivalenol: Mechanisms of action, human exposure, and toxicological relevance. Archives of Toxicology, 84, 663–679.CrossRefGoogle Scholar
  56. Ribichich, K. F., Lopez, S. E., & Vegetti, A. C. (2000). Histopathological spikelet changes produced by Fusarium graminearum in susceptible and resistant wheat cultivars. Plant Disease, 84, 794–802.CrossRefGoogle Scholar
  57. Rotter, B. A., Prelusky, D. B., & Pestka, J. J. (1996). Toxicology of deoxynivalenol (vomitoxin). Journal of Toxicology and Environmental Health, 48, 1–34.CrossRefGoogle Scholar
  58. Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings 3th Earth Resources Technology Satellite-1 Symposium, Goddard Space Flight Center, 10–14 December 1973, NASA, Washington, D.C. (USA), 309–317.Google Scholar
  59. Rumpf, T., Mahlein, A.-K., Steiner, U., Oerke, E.-C., Dehne, H.-W., & Plümer, L. (2010). Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74, 91–99.CrossRefGoogle Scholar
  60. Salgado, J. D., Madden, L. V., & Paul, P. A. (2015). Quantifying the effects of Fusarium head blight on grain yield and test weight in soft red winter wheat. Phytopathology, 105, 295–306.CrossRefGoogle Scholar
  61. Schroeder, H. W., & Christensen, J. J. (1963). Factors affecting resistance of wheat to scab caused by Gibberella zeae. Phytopathology, 53, 831–838.Google Scholar
  62. Serrano, L., Peñuelas, J., & Ustin, S. L. (2002). Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals. Remote Sensing of Environment, 81, 355–364.CrossRefGoogle Scholar
  63. Simko, I., & Piepho, H.-P. (2012). The area under the disease progress stairs: Calculation, advantage, and application. Phytopathology, 102, 381–389.CrossRefGoogle Scholar
  64. Sims, D. A., & Gamon, J. A. (2002). Relationship between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81, 337–354.CrossRefGoogle Scholar
  65. Singh, A., Ganapathysubramanian, B., Singh, A. K., & Sarkar, S. (2016). Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science, 21, 110–124.CrossRefGoogle Scholar
  66. Siuda, R., Grabowski, A., Lenc, L., Ralcewicz, M., & Spychaj-Fabisiak, E. (2010). Influence of the degree of fusariosis on technological traits of wheat grain. International Journal of Food Science and Technology, 45, 2596–2604.CrossRefGoogle Scholar
  67. Stack, R. W., & McMullen, M. P. (1995). A visual scale to estimate severity of Fusarium head blight in wheat. North Dakota State University Extension Service, PP-1095.Google Scholar
  68. Steiner, B., Buerstmayr, M., Michel, S., Schweiger, W., Lemmens, M., & Buerstmayr, H. (2017). Breeding strategies and advances in line selection for Fusarium head blight resistance in wheat. Tropical Plant Pathology, 42, 165–174.CrossRefGoogle Scholar
  69. Talas, F., Parzies, H. K., & Miedaner, T. (2011). Diversity in genetic structure and chemotype composition of Fusarium graminearum sensu stricto populations causing wheat head blight in individual fields in Germany. European Journal of Plant Pathology, 131, 39–48.CrossRefGoogle Scholar
  70. Thomas, S., Wahabzada, M., Kuska, M.-T., Rascher, U., & Mahlein, A.-K. (2016). Observation of plant–pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements. Functional Plant Biology, 44, 23–34.CrossRefGoogle Scholar
  71. Thomas, S., Kuska, M.-T., Bohnenkamp, D., Brugger, A., Alisaac, E., Wahabzada, M., Behmann, J., & Mahlein, A.-K. (2018). Benefits of hyperspectral imaging for plant disease detection and plant protection: A technical perspective. Journal of Plant Diseases and Protection, 125, 5–20.CrossRefGoogle Scholar
  72. Trail, F. (2009). For blighted waves of grain: Fusarium graminearum in the postgenomics era. Plant Physiology, 149, 103–110.CrossRefGoogle Scholar
  73. Wang, J., Wieser, H., Pawelzik, E., Weinert, J., Keutgen, A. J., & Wolf, G. A. (2005). Impact of the fungal protease produced by Fusarium culmorum on the protein quality and breadmaking properties of winter wheat. European Food Research and Technology, 220, 225–259.CrossRefGoogle Scholar
  74. Ward, T. J., Clear, R. M., Rooney, A. P., O’Donnell, K., Gaba, D., Patrick, S., Starkey, D. E., Gilbert, J., Geiser, D. M., & Nowicki, T. W. (2008). An adaptive evolutionary shift in Fusarium head blight pathogen populations is driving the rapid spread of more toxigenic Fusarium graminearum in North America. Fungal Genetics and Biology, 45, 473–484.CrossRefGoogle Scholar
  75. Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, article ID, 1353691 17 Pages.Google Scholar

Copyright information

© Koninklijke Nederlandse Planteziektenkundige Vereniging 2018

Authors and Affiliations

  • E. Alisaac
    • 1
  • J. Behmann
    • 1
  • M. T. Kuska
    • 1
  • H.-W. Dehne
    • 1
  • A.-K. Mahlein
    • 1
    • 2
  1. 1.Institute of Crop Science and Resource Conservation (INRES), Plant Diseases and Plant ProtectionRheinische Friedrich-Wilhelms Universität BonnBonnGermany
  2. 2.Institute of Sugar Beet Research (IfZ)GöttingenGermany

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