Advertisement

Precision Agriculture

, Volume 13, Issue 2, pp 236–255 | Cite as

Detection of bacterial wilt infection caused by Ralstonia solanacearum in potato (Solanum tuberosum L.) through multifractal analysis applied to remotely sensed data

  • Perla Chávez
  • Christian Yarlequé
  • Hildo Loayza
  • Víctor Mares
  • Paola Hancco
  • Sylvie Priou
  • María del Pilar Márquez
  • Adolfo Posadas
  • Percy Zorogastúa
  • Jaume Flexas
  • Roberto QuirozEmail author
Article

Abstract

Potato bacterial wilt, caused by the bacterium Ralstonia solanacearum race 3 biovar 2 (R3bv2), affects potato production in several regions in the world. The disease becomes visually detectable when extensive damage to the crop has already occurred. Two greenhouse experiments were conducted to test the capability of a remote sensing diagnostic method supported by multispectral and multifractal analyses of the light reflectance signal, to detect physiological and morphological changes in plants caused by the infection. The analysis was carried out using the Wavelet Transform Modulus Maxima (WTMM) combined with the Multifractal (MF) analysis to assess the variability of high-resolution temporal and spatial signals and the conservative properties of the processes across temporal and spatial scales. The multispectral signal, enhanced by multifractal analysis, detected both symptomatic and latently infected plants, matching the results of ELISA laboratory assessment in 100 and 82%, respectively. Although the multispectral method provided no earlier detection than the visual assessment on symptomatic plants, the former was able to detect asymptomatic latent infection, showing a great potential as a monitoring tool for the control of bacterial wilt in potato crops. Applied to precision agriculture, this capability of the remote sensing diagnostic methodology would provide a more efficient control of the disease through an early and full spatial assessment of the health status of the crop and the prevention of spreading the disease.

Keywords

Remote sensing diagnostic method Visual monitoring Multispectral analysis Wavelet transform Precision agriculture 

Notes

Acknowledgments

Support for this work was provided by the International Foundation for Science (IFS Grant 4068/-I), the Production Systems and the Environment Division of the International Potato Center (CIP) and the CIP-ALTAGRO project. The authors thank Eng. Liliam Gutarra from the Integrated Crop Management Division at CIP for her support on laboratory assessments, and to R.T.J. McAteer and collaborators for kindly sharing their wavelet-multifractal algorithm. P. Chávez gives special thanks to Arnauld A. Thiry for his permanent and unconditional support, and Drs. Salomón Helfgott and Vicente Rázuri from La Molina Agricultural University for their good advices.

References

  1. Agrios, G. N. (2005). Plant pathology (5th ed.). San Diego, CA: Academic Press.Google Scholar
  2. Allen, C., Kelman, A., & French, E. R. (2001). Brown rot of potatoes. In W. R. Stevenson, R. Loria, G. D. Franc, & D. P. Weingartner (Eds.), Compendium of potato diseases (pp. 11–13). St. Paul, MN: The American Phytopathological Society.Google Scholar
  3. Arneodo, A., Bacry, E., Graves, P. V., & Muzzy, J. F. (1995). Characterizing long-range correlations in DNA sequences from wavelet analysis. Physical Review Letters, 74(16), 3293–3296.PubMedCrossRefGoogle Scholar
  4. Bacry, E., Muzy, J. F., & Arnéodo, A. (2003). Singularity spectrum of fractal signals from wavelet analysis: Exact results. Journal of Statistical Physics, 70(3–4), 635–674.Google Scholar
  5. Blackburn, G. A., & Ferwerda, J. G. (2008). Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis. Remote Sensing of Environment, 112(4), 1614–1632.CrossRefGoogle Scholar
  6. Broge, N. H., & Leblanc, E. (2000). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76(2), 156–172.CrossRefGoogle Scholar
  7. Buchanan-Wollaston, V. (1997). The molecular biology of leaf senescense. Journal of Experimental Botany, 48(2), 181–199.CrossRefGoogle Scholar
  8. Byalovskii, Y. Y., Bulatetskii, S. V., & Suchkova, Z. V. (2005). Heart rate variability and fractal neurodynamics during local magnetic vibroacoustic treatment. Human Physiology, 31(4), 413–420. Translated from Fiziologiya Cheloveka, 431(414), 450–460. Google Scholar
  9. CABI. (2003). Crop protection compendium: Global module (5th ed.). Wallingford, UK: CAB International.Google Scholar
  10. Chávez, P., Yarlequé, C., Piro, O., Posadas, A., Mares, V., Loayza, H., et al. (2010). Applying multifractal analysis to remotely sensed data for assessing PYVV infection in potato (Solanum tuberosum L.) crops. Remote Sensing Journal, 2(5), 1197–1216.CrossRefGoogle Scholar
  11. Chávez, P., Zorogastúa, P., Chuquillanqui, C., Salazar, L. F., Mares, V., & Quiroz, R. (2009). Assessing Potato Yellow Vein Virus (PYVV) infection using remotely sensed data. International Journal of Pest Management, 55, 251–256.CrossRefGoogle Scholar
  12. Chhabra, A. B., Jensen, R. V., & Sreenivasan, K. R. (1989a). Extraction of underlying multiplicative processes from multifractals via the thermodynamic formalism. Physical Review A, 40(8), 4593–4611.PubMedCrossRefGoogle Scholar
  13. Chhabra, A. B., Meneveu, C., Jensen, R. V., & Sreenivasan, K. R. (1989b). Direct determination of the f(a) singularity spectrum and its application to fully developed turbulence. Physical Review A, 40(9), 5284–5294.PubMedCrossRefGoogle Scholar
  14. Chiwaki, K., Nagamori, S., & Inoue, Y. (2005). Predicting bacterial wilt disease of tomato plants using remotely sensed thermal imagery. Journal of Agricultural Meteorology, 61, 153–164.CrossRefGoogle Scholar
  15. CIP-International Potato Center. (2008). Review of nematology activities at CIP (p. 16). https://research.cip.cgiar.org/confluence/download/attachments/16679035/Report+on+Nematology+at+CIP+1999+Author+Maria+Scurrah+-1.pdf?version=1. Accessed 1 March 2010.
  16. Cook, D., Barlow, E., & Sequeira, L. (1989). Genetic diversity of Pseudomonas solanacearum: Detection of restriction fragment length polymorphism with DNA probes that specify virulence and the hypersensitive response. Molecular Plant-Microbe Interactions, 2, 113–121.CrossRefGoogle Scholar
  17. Cook, D., & Sequeira, L. (1994). Strain differentiation of Pseudomonas solanacearum by molecular genetic methods. In A. C. Hayward & G. L. Hatman (Eds.), Bacterial Wilt: The disease and its causative agent, Pseudomonas solanacearum (pp. 77–93). Wallingford, UK: CAB International.Google Scholar
  18. Crippen, R. E. (1990). Calculating the vegetation index faster. Remote Sensing of Environment, 34(1), 71–73.CrossRefGoogle Scholar
  19. Daughtry, C. S. T., Walthall, C. L., Kim, M. S., Brown de Colstoun, E., & McMurtrey III, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2), 229–239.CrossRefGoogle Scholar
  20. Elphinstone, J. G. (1996). Survival and possibilities for extinction of Pseudomonas solanacearum (Smith) in cool climates. Potato Research, 39, 403–410.CrossRefGoogle Scholar
  21. Fock, I., Collonnier, C., Luisetti, J., Purwito, A., Souvannavong, V., Vedel, F., et al. (2001). Use of Solanum stenotomum for introduction of resistance to bacterial wilt in somatic hybrids of potato. Plant Physiology and Biochemistry, 39, 899–908.CrossRefGoogle Scholar
  22. French, E. R., Gutarra, L., Aley, P., & Elphinstone, J. (1995). Culture media for Pseudomonas solanacearum: Isolation, identification and maintenance. Fitopatologia, 30, 126–130.Google Scholar
  23. Gamon, J. A., Peñuelas, J., & Field, C. B. (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41(1), 35–44.CrossRefGoogle Scholar
  24. Grimault, V., Gelie, B., Lemattre, M., Prior, P., & Schmit, J. (1994). Comparative histology of resistant and susceptible tomato cultivars infected by Pseudomonas solanacearum. Physiological and Molecular Plant Pathology, 44, 105–123.CrossRefGoogle Scholar
  25. Habashy, W. H. S., Fawzi, F. G., El-Huseiny, T. M., & Neweigy, N. A. (1993). Bacterial wilt of potatoes. II. Sensitivity of the pathogen to antibiotics and pathogenesis by streptomycin-resistant mutants. Egyptian Journal of Agricultural Research, 71, 401–412.Google Scholar
  26. Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrowband vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2–3), 416–426.CrossRefGoogle Scholar
  27. Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3), 337–352.CrossRefGoogle Scholar
  28. Halsey, T. C., Jensen, M. H., Kanadoff, L. P., Procaccia, I., & Shraiman, B. (1986). Fractal measures and their singularities: The characterization of strange sets. Physical Review A, 33(2), 1141–1151.PubMedCrossRefGoogle Scholar
  29. Hartman, G. L., & Elphinstone, J. G. (1994). Advances in the control of Pseudomonas solanacearum Race 1 in major food crops. In A. C. Hayward & G. L. Hatman (Eds.), Bacterial Wilt: The disease and its causative agent, Pseudomonas solanacearum (pp. 157–177). Wallingford, UK: CAB International.Google Scholar
  30. Hayward, A. C. (1964). Characteristics of Pseudomonas solanacearum. Journal of Applied Bacteriology, 27, 265–277.CrossRefGoogle Scholar
  31. Hayward, A. C. (1991). Biology and epidemiology of bacterial wilt caused by Pseudomonas solanacearum. Annual review of Phytopathology, 29, 65–87.PubMedCrossRefGoogle Scholar
  32. Hernández, Y., Marino, N., Trujillo, G., & Urbina de Navarro, C. (2005). Invasión de Ralstonia solanacearum en tejidos de tallos de tomate (Lycopersicon esculentum Mill). Revista de la Facultad de Agronomía, 22(2), 185–194.Google Scholar
  33. Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309.CrossRefGoogle Scholar
  34. Inoue, Y. (1990). Remote detection of physiological depression in crop plants with infrared thermal imagery. Japanese Journal of Crop Science, 59, 762–768.CrossRefGoogle Scholar
  35. Ivanov, P. C., Nunes Amaral, L. A., Goldberger, A. L., Havlin, S., Rosenblum, M. G., Struzik, Z. R., et al. (1999). Multifractality in human heartbeat dynamics. Nature, 399, 461–465.PubMedCrossRefGoogle Scholar
  36. Jaffard, S. (2004). Wavelet techniques in multifractal analysis, fractal geometry and applications. In: AMS (Ed.), Proceedings of symposia in pure mathematics (pp. 91–152). Providence, RI.Google Scholar
  37. Janse, J. D. (1996). Potato brown rot in Western Europe—History, present occurrence and some remarks on possible origin, epidemiology and control strategies. Bulletin OEPP, 26, 679–695.Google Scholar
  38. Latka, M., Glaubic-Latka, M., Latka, D., & West, B. (2002). The loss of multifractality in migraines. http://arxiv.org/PS_cache/physics/pdf/0204/0204010v1.pdf.
  39. López, M. M., & Biosca, E. G. (2004). Potato bacterial wilt management: New prospects for an old problem. In C. Allen, P. Prior, & A. C. Hayward (Eds.), Bacterial wilt disease and the Ralstonia species complex (pp. 205–224). St. Paul, MN: APS Press.Google Scholar
  40. McAteer, R. T. J., Young, C. A., Ireland, J., & Gallagher, P. T. (2007). The bursty nature of solar flare X-ray emission. The Astrophysical Journal, 662, 691–700.CrossRefGoogle Scholar
  41. Mendoza, H. A. (1994). Development of potatoes with multiple resistance to biotic and abiotic stresses: The International Potato Center Approach. In G. W. Zehnder, M. L. Powelson, & R. Jansson (Eds.), Advances in potato pest biology and management (pp. 627–642). St. Paul, MN: American Phytopathological Society.Google Scholar
  42. Murakoshi, S., & Takahashi, M. (1984). Trials of some control of tomato wilt caused by Pseudomonas solanacearum. Bulletin of the Kanagawa Horticultural Experiment Station, 31, 50–56.Google Scholar
  43. Muzy, J. F., Bacry, E., & Arneodo, A. (1991). Wavelets and multifractal formalism for singular signals: Application to turbulence data. Physical Review Letters, 67, 3515–3518.PubMedCrossRefGoogle Scholar
  44. Parrott, N., & Kalibwani, F. (2004). Organic agriculture in the continents, Africa. In H. Willer & M. Yussefi (Eds.), The world of organic agriculture statistics and emerging trends (pp. 55–68). Bonn, Germany: International Federation of Organic Agriculture Movements.Google Scholar
  45. Polikar, R. (1996). The wavelet tutorial. http://users.rowan.edu/~polikar/WAVELETS/WTpart1.html. Accessed 1 March 2010.
  46. Posadas, A. N. D., Giménez, D., Quiroz, R. A., & Protz, R. (2003). Multifractal characterization of soil pore systems. Soil Science Society of America Journal, 67, 1361–1369.CrossRefGoogle Scholar
  47. Posadas, A. N. D., Quiroz, R., Zorogastúa, P., & León-Velarde, C. (2005). Multifractal characterization of the spatial distribution of Ulexite in a Bolivian salt flat. International Journal of Remote Sensing, 26, 615–627.CrossRefGoogle Scholar
  48. Prior, P., & Fegan, M. (2005). Recent developments in the phylogeny and classification of Ralstonia solanacearum. Acta Horticulturae (ISHS), 695, 127–136.Google Scholar
  49. Priou, S., Gutarra, L., & Aley, P. (1999). Highly sensitive detection of Ralstonia solanacearum in latently infected potato tubers by post-enrichment enzyme-linked immunosorbent assay on nitrocellulose membrane. EPPO/OEPP Bulletin, 29, 117–125.Google Scholar
  50. Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95–107.CrossRefGoogle Scholar
  51. Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W., & Harlan, J. C. (1974). Monitoring the vernal advancement of retrogradation (green wave effect) of natural vegetation. Final Report, Type III, NASA/GSFC, Greenbelt, MD, p. 371.Google Scholar
  52. Schaad, N. W. (1988). Laboratory guide for identification of plant pathogenic bacteria (164 pp.). St. Paul, MN: American Phytopathological Society.Google Scholar
  53. Schertzer, D., & Lovejoy, S. (1989). Nonlinear variability in geophysics: Multifractal analysis and simulation. In L. Pietronero (Ed.), Fractals: Physical origin and consequences (pp. 49–79). New York: Plenum.Google Scholar
  54. Schertzer, D., & Lovejoy, S. (2004). Uncertainty and predictability in geophysics: Chaos and multifractal insights. In R. S. J. Sparks & C. J. Hawkesworth (Eds.), State of the planet, frontiers and challenges in geophysics (pp. 317–334). Washington DC: American Geophysical Union.CrossRefGoogle Scholar
  55. Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. Urbana, IL: University of Illinois Press.Google Scholar
  56. Swanson, J. K., Yao, J., Tans-Kersten, J., & Allen, C. (2005). Behavior of Ralstonia solanacearum race 3 biovar 2 during latent and active infection of geranium. Phytopathology, 95, 136–143.PubMedCrossRefGoogle Scholar
  57. Sylvander, B., & Le Floc’h-Wadel, A. (2000). Consumer demand and production of organics in the EU. AgBioForum, 3, 97–106.Google Scholar
  58. University of Arizona. (2005). Remote sensing of vegetation. http://rangeview.arizona.edu/Tutorials/intro.asp. Accessed 9 May 2011.
  59. van Elsas, J. D., Kastelein, P., de Vries, P. M., & van Overbeek, L. S. (2001). Effects of ecological factors on the survival and physiology of Ralstonia solanacearum bv. 2 in irrigation water. Canadian Journal of Microbiology, 47, 842–854.PubMedGoogle Scholar
  60. van Elsas, J. D., Kastelein, P., van Bekkum, P., van der Wolf, J. M., de Vries, P. M., & van Overbeek, L. S. (2000). Survival of Ralstonia solanacearum biovar 2, the causative agent of potato brown rot, in field and microcosm soils in temperate climates. Phytopathology, 90, 1358–1366.PubMedCrossRefGoogle Scholar
  61. Vicsek, T. (1992). Fractal growth phenomena (2nd ed.). Singapore: Word Scientific Publishing Co.Google Scholar
  62. Voss, R. F. (1988). Fractals in nature: From characterization to simulation. In H.-O. Peitgen & D. Saupe (Eds.), The science of fractal images (pp. 21–70). New York: Springer.CrossRefGoogle Scholar
  63. Wada, M., Kagawa, T., & Sato, Y. (2003). Chloroplast movement. Annual Review of Plant Biology, 54, 455–468.PubMedCrossRefGoogle Scholar
  64. Weingartner, D. P., & Shumaker, J. R. (1988). In row injection of metham sodium and other soil fumigants for control of nematodes and soil borne potato diseases in Florida. 72nd Annual Meeting of the Potato Association of America, Fort Collins, Colorado, USA. American Potato Journal, 65, 504.Google Scholar
  65. Williams, G. C. (1999). Pleiotropy, natural selection and the evolution of aging. Evolution, 11, 398–411.CrossRefGoogle Scholar
  66. Williamson, L., Nakaho, K., Hudelson, B., & Allen, C. (2002). Ralstonia solanacearum race 3, biovar 2 strains isolated from geranium are pathogenic on potato. Plant Disease, 86, 987–991.CrossRefGoogle Scholar
  67. Wolfinger, R. D., & Chang, M. (1998). Comparing the SAS GLM and MIXED procedures for repeated measures. Cary, NC: SUGI Proceedings.Google Scholar
  68. Yu, Z.-G., Anh, V., & Lau, K.-S. (2001). Multifractal characterisation of length sequences of coding and noncoding segments in a complete genome. Physica A: Statistical Mechanics and its Applications, 301, 351–361.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Perla Chávez
    • 1
    • 2
  • Christian Yarlequé
    • 1
  • Hildo Loayza
    • 1
  • Víctor Mares
    • 1
  • Paola Hancco
    • 1
  • Sylvie Priou
    • 1
  • María del Pilar Márquez
    • 1
  • Adolfo Posadas
    • 1
  • Percy Zorogastúa
    • 1
  • Jaume Flexas
    • 2
  • Roberto Quiroz
    • 1
    Email author
  1. 1.Crop Management and Production Systems DivisionInternational Potato CenterLima 12Peru
  2. 2.Research Group in Biology of Plants Under Mediterranean ConditionsUniversity of Balearic IslandsPalma de MallorcaSpain

Personalised recommendations