Skip to main content
SpringerLink
Log in

Disease Gradients of Late Blight of Potato from Infrared Images of Commercial Fields

  • Published:
American Journal of Potato Research Aims and scope Submit manuscript

Abstract

Controlled inoculation studies of dispersal in situ are often not possible due to the presence of background inoculum, as is the case with late blight of potato (caused by Phytophthora infestans) in the Columbia Basin in Washington. Six disease gradients were quantified from forty-eight infrared images of infected potato fields. The mean pixel value was recorded as a proxy for disease severity. Images taken at multiple dates revealed steeper gradients at the earliest date, suggesting less background infection at that time. The aggregated disease gradients were best fit by y = 3.82*105 (x + 5.94)−2.36 modified inverse power (MIP) function and y = 424.81e−0.034x exponential function. Simulations governed by the MIP function progressed faster than those governed by the exponential function across a range of input parameters. This research demonstrates the potential to describe dispersal in systems in which controlled experiments are not possible, and it provides a tool to control late blight of potato epidemics.

Resumen

Los estudios con inoculación controlada de la dispersión in situ a menudo no son posibles debido a la presencia de inóculo del ambiente, como en el caso del tizón tardío de la papa (causado por Phytophthora infestans) en la rivera del Columbia en Washington. Se cuantificaron seis gradientes de enfermedad de 48 imágenes infrarrojas de campos de papa infectados. El valor medio del pixel se registró como una representación para la severidad de la enfermedad. Las imágenes tomadas en múltiples fechas revelaron gradientes mas pronunciados en la fecha mas temprana, lo que sugirió menos infección del ambiente en ese tiempo. Los gradientes agregados de la enfermedad se ajustaron mejor por la función y = 3.82*105(x + 5.94)−2.36 de poder inverso modificado (MIP), y por la función exponencial y = 424.81e−0.034x. Las simulaciones gobernadas por la función MIP progresaron mas rápido que aquellas gobernadas por la función exponencial a lo largo de una amplitud de parámetros introducidos. Esta investigación demuestra el potencial para describir la dispersión en sistemas en los cuales no son posibles los experimentos controlados, y proporciona una herramienta para controlar la epidemia del tizón tardío de la papa.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Andrade-Piedra, J.L., R.J. Hijmans, G.A. Forbes, W.E. Fry, and R.J. Nelson. 2005. Simulation of potato late blight in the Andes. I: Modification and Parameterization of the LATEBLIGHT Model. Phytopathology 95: 1191–1199.

    PubMed  Google Scholar 

  • Andrivon, D., F. Pilet, J. Montarry, M. Hafidi, R. Corbière, E.H. Achbani, R. Pellé, and D. Ellissèche. 2007. Adaptation of Phytophthora infestans to partial resistance in potato: Evidence from French and Moroccan populations. Phytopathology 97: 338–343.

    PubMed  Google Scholar 

  • Aylor, D.E. 1986. A framework for examining inter-regional aerial transport of fungal spores. Agricultural and Forest meteorology, 38(4), 263-288. Agricultural and Forest Meteorology 38: 263–288.

    Google Scholar 

  • Aylor, D.E. 1987. Deposition gradients of Urediniospores of Puccinia recondita near a source. Phytopathology 77: 1442–1448.

  • Aylor, D.E. 1990. The role of intermittent wind in the dispersal of fungal pathogens. Annual Review of Phytopathology 28: 73–92.

    Google Scholar 

  • Aylor, D.E. 1999. Biophysical scaling and the passive dispersal of fungus spores: Relationship to integrated pest management strategies. Agricultural and Forest Meteorology 97: 275–292.

    Google Scholar 

  • Beck, R.A. 2003. Remote sensing and GIS as counterterrorism tools in the Afghanistan war: A case study of the Zhawar Kili region. The Professional Geographer 55: 170–179.

    Google Scholar 

  • Bock, C.H., A.Z. Cook, P.E. Parker, T.R. Gottwald, and J.H. Graham. 2012. Short-distance dispersal of splashed bacteria of Xanthomonas citri subsp. citri from canker-infected grapefruit tree canopies in turbulent wind: Dispersal plume of Xanthomonas citri subsp. citri. Plant Pathology 61: 829–836.

    Google Scholar 

  • Bradski, G. 2019. The OpenCV library (version 4.2.0).

    Google Scholar 

  • Brown, J.K.M. 2002. Aerial dispersal of pathogens on the global and continental scales and its impact on plant disease. Science 297: 537–541.

    CAS  PubMed  Google Scholar 

  • Bullock, J.M., and R.T. Clarke. 2000. Long distance seed dispersal by wind: Measuring and modelling the tail of the curve. Oecologia 124: 506–521.

    CAS  PubMed  Google Scholar 

  • Castillo-Chavez, C., and A.A. Yakubu. 2001. Dispersal, disease and life-history evolution. Mathematical Biosciences 173: 35–53.

    CAS  PubMed  Google Scholar 

  • Codling, E.A., M.J. Plank, and S. Benhamou. 2008. Random walk models in biology. Journal of the Royal Society Interface 5: 813–834.

    PubMed Central  Google Scholar 

  • Cooke, D.E.L., A. Drenth, J.M. Duncan, G. Wagels, and C.M. Brasier. 2000. A molecular phylogeny of Phytophthora and related Oomycetes. Fungal Genetics and Biology 30: 17–32.

    CAS  PubMed  Google Scholar 

  • Cooke, L.R., H.T.A.M. Schepers, A. Hermansen, R.A. Bain, N.J. Bradshaw, F. Ritchie, D.S. Shaw, et al. 2011. Epidemiology and integrated control of potato late blight in Europe. Potato Research 54: 183–222.

    Google Scholar 

  • Costes, E., P.E. Lauri, S. Simon, and B. Andrieu. 2013. Plant architecture, its diversity and manipulation in agronomic conditions, in relation with pest and pathogen attacks. European Journal of Plant Pathology 135: 455–470.

    Google Scholar 

  • Cousens, R.D., B.D. Hughes, and M.B. Mesgaran. 2018. Why we do not expect dispersal probability density functions based on a single mechanism to fit real seed shadows. Journal of Ecology 106: 903–906.

    Google Scholar 

  • Cunniffe, N.J., R.O.J.H. Stutt, F. van den Bosch, and C.A. Gilligan. 2011. Time-dependent infectivity and flexible latent and infectious periods in compartmental models of plant disease. Phytopathology 102: 365–380.

    Google Scholar 

  • Farber, D.H., J. Medlock, and C.C. Mundt. 2017. Local dispersal of Puccinia striiformis f. sp. tritici from isolated source lesions. Plant Pathology 66: 28–37.

    CAS  PubMed  Google Scholar 

  • Fry, W.E. 2007. The canon of potato science: 10. Late blight and early blight. Potato Research 50: 243–245.

    Google Scholar 

  • Gongora-Canul, C., J.D. Salgado, D. Singh, A.P. Cruz, L. Cotrozzi, J. Couture, M.G. Rivadeneira, G. Cruppe, B. Valent, T. Todd, J. Poland, and C.D. Cruz. 2020. Temporal dynamics of wheat blast epidemics and disease measurements using multispectral imagery. Phytopathology 110: 393–405.

  • Holland, J.D. 2009. Dispersal kernel determines symmetry of spread and geographical range for an insect. International Journal of Ecology 2009: 1–4.

    Google Scholar 

  • Huang, C.-M., D.-J. Liao, H.-S. Wu, W.-C. Shen, and C.-L. Chung. 2016. Cyclone-based spore trapping, quantitative real-time polymerase chain reaction and high resolution melting analysis for monitoring airborne inoculum of Magnaporthe oryzae. Annals of Applied Biology 169: 75–90.

    CAS  Google Scholar 

  • Hughes, G., F. van den Bosch, and L.V. Madden. 2007. The study of plant disease epidemics. St. Paul: St. Paul : American Phytopathological Society.

  • Ibarra-Castanedo, C., D. González, M. Klein, M. Pilla, S. Vallerand, and X. Maldague. 2004. Infrared image processing and data analysis. Infrared Physics & Technology 46: 75–83.

    Google Scholar 

  • Johnson, D.A., T.F. Cummings, P.B. Hamm, R.C. Rowe, J.S. Miller, R.E. Thornton, G.Q. Pelter, and E.J. Sorensen. 1997. Potato late blight in the Columbia Basin: An economic analysis of the 1995 epidemic. Plant Disease 81: 103–106.

    CAS  PubMed  Google Scholar 

  • Johnson, D.A., J.R. Alldredge, and P.B. Hamm. 1998. Expansion of potato late blight forecasting models for the Columbia Basin of Washington and Oregon. Plant Disease 82: 642–645.

    PubMed  Google Scholar 

  • Johnson, D.A., J.R. Alldredge, P.B. Hamm, and Bruce E. Frazier. 2003. Aerial photography used for spatial pattern analysis of late blight infection in irrigated potato circles. Phytopathology 93: 805–812.

    PubMed  Google Scholar 

  • Jones, E., E. Oliphant, P. Peterson, et al. 2001. SciPy: Open source scientific tools for Python.

    Google Scholar 

  • Kamoun, S., F. Oliver, J.D.G. Jones, H.S. Judelson, G.S. Ali, R.J.D. Dalio, S.G. Roy, et al. 2015. The top 10 oomycete pathogens in molecular plant pathology. Molecular Plant Pathology 16: 413–434.

    PubMed  Google Scholar 

  • Kermack, W.O., and A.G. McKendrick. 1927. A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A 115: 700–721.

    Google Scholar 

  • Kermack, W.O., A.G. McKendrick, and G.T. Walker. 1927. A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character 115: 700–721.

    Google Scholar 

  • Khan, M.J., H.S. Khan, A. Yousaf, K. Khurshid, and A. Abbas. 2018. Modern trends in Hyperspectral image analysis: A review. IEEE Access 6: 14118–14129.

    Google Scholar 

  • Kirk, W.W., K.J. Felcher, D.S. Douches, J. Coombs, J.M. Stein, K.M. Baker, and R. Hammerschmidt. 2001. Effect of host plant resistance and reduced rates and frequencies of fungicide application to control potato late blight. Plant Disease 85: 1113–1118.

    CAS  PubMed  Google Scholar 

  • Kot, M., M.A. Lewis, and P. van den Driessche. 1996. Dispersal data and the spread of invading organisms. Ecology 77: 2027–2042.

    Google Scholar 

  • Kot, M., J. Medlock, T. Reluga, and D.B. Walton. 2004. Stochasticity, invasions, and branching random walks. Theoretical Population Biology 66: 175–184.

    PubMed  Google Scholar 

  • Krezhova, D., B. Dikova, and S. Maneva. 2014. Ground based hyperspectral remote sensing for disease detection of tobacco plants. Bulgarian Journal of Agricultural Science 20: 1142–1150.

    Google Scholar 

  • Mahlein, A.K., E.C. Oerke, U. Steiner, and H.W. Dehne. 2012. Recent advances in sensing plant diseases for precision crop protection. European Journal of Plant Pathology 133: 197–209.

    CAS  Google Scholar 

  • The MathWorks Inc. 2018. MATLAB (version R2018a). Massachusetts: Natick.

    Google Scholar 

  • Matta, C. 2010. Spontaneous generation and disease causation: Anton de Bary’s experiments with Phytophthora infestans and late blight of potato. Journal of the History of Biology 43: 459–491.

    PubMed  Google Scholar 

  • McBratney, A., B. Whelan, T. Ancev, and J. Bouma. 2005. Future directions of precision agriculture. Precision Agriculture 6: 7–23.

  • van der Meer, F.D., H.M.A. van der Werff, F.J.A. van Ruitenbeek, C.A. Hecker, W.H. Bakker, M.F. Noomen, M. van der Meijde, E.J.M. Carranza, J. Boudewijn de Smeth, and T. Woldai. 2012. Multi- and hyperspectral geologic remote sensing: A review. International Journal of Applied Earth Observation and Geoinformation 14: 112–128.

    Google Scholar 

  • Mizubuti, E.S.G., D.E. Aylor, and W.E. Fry. 2000. Survival of Phytophthora infestans sporangia exposed to solar radiation. Phytopathology 90: 78–84.

    CAS  PubMed  Google Scholar 

  • Mundt, C.C. 1989. Use of the modified Gregory model to describe primary disease gradients of wheat leaf rust produced from area sources of inoculum. Phytopathology 79: 241–246.

    Google Scholar 

  • Mundt, C.C., and K.J. Leonard. 1985. A modification of Gregory’s model for describing plant disease gradients. Phytopathology 75: 930–935.

    Google Scholar 

  • Mundt, C.C., K.E. Sackett, L.D. Wallace, C. Cowger, and J.P. Dudley. 2009. Long-distance dispersal and accelerating waves of disease: Empirical relationships. The American Naturalist 173: 456–466.

    PubMed  Google Scholar 

  • Nagol, J.R., E.F. Vermote, and S.D. Prince. 2009. Effects of atmospheric variation on AVHRR NDVI data. Remote Sensing of Environment 113: 392–397.

    Google Scholar 

  • Nathan, R. 2001. The challenges of studying dispersal. Trends in Ecology & Evolution 16: 481–483.

    CAS  Google Scholar 

  • Nathan, R. 2006. Long-distance dispersal of plants. Science 313: 786–788.

    CAS  PubMed  Google Scholar 

  • O’Hara, Robert B., and D. Johan Kotze. 2010. Do not log-transform count data. Methods in Ecology and Evolution 1: 118–122.

    Google Scholar 

  • Owens, O.H., and D.T. Krizek. 1980. Multiple effects of Uv radiation (265–330 nm) on fungal spore emergence. Photochemistry and Photobiology 32: 41–49.

    Google Scholar 

  • Python Software Foundation. 2016. Python Language Reference (version 3.6).

  • R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing (version 3.5). Vienna, Austria.

  • Reynolds, A.M. 2011. Exponential and power-law contact distributions represent different atmospheric conditions. Phytopathology 101: 1465–1470.

    CAS  PubMed  Google Scholar 

  • Rieux, A., S. Soubeyrand, F. Bonnot, E.K. Klein, J.E. Ngando, A. Mehl, V. Ravigne, J. Carlier, and L. de Lapeyre de Bellaire. 2014. Long-distance wind-dispersal of spores in a fungal plant pathogen: Estimation of anisotropic dispersal kernels from an extensive field experiment. Edited by R.A. Wilson. PLoS ONE 9: e103225.

    Google Scholar 

  • Sackett, K.E., and C.C. Mundt. 2005. Primary disease gradients of wheat stripe rust in large field plots. Phytopathology 95: 983–991.

    PubMed  Google Scholar 

  • Sandino, J., F. Gonzalez, K. Mengersen, and K.J. Gaston. 2018. UAVs and Machine Learning Revolutionising Invasive Grass and Vegetation Surveys in Remote Arid Lands. In UAVs and machine learning Revolutionising invasive grass and vegetation surveys in remote arid lands. Sensors.

    Google Scholar 

  • Scholthof, K.B.G. 2007. The disease triangle: Pathogens, the environment and society. Nature Reviews Microbiology 5: 152–156.

    CAS  PubMed  Google Scholar 

  • Severns, P.M., K.E Sackett, D.H. Farber, and C.C. Mundt. 2019. Consequences of long-distance dispersal for epidemic spread: Patterns, scaling, and mitigation. Plant Disease 103: 177–191.

  • Shaw, M.W. 1995. Simulation of population expansion and spatial pattern when individual dispersal distributions do not decline exponentially with distance. Proceedings of the Royal Society of London. Series B: Biological Sciences 259: 243–248.

    Google Scholar 

  • Shaw, M.W., T.D. Harwood, M.J. Wilkinson, and L. Elliott. 2006. Assembling spatially explicit landscape models of pollen and spore dispersal by wind for risk assessment. Proceedings of the Royal Society B: Biological Sciences 273: 1705–1713.

    CAS  PubMed  Google Scholar 

  • Smith, L.C. 1997. Satellite remote sensing of river inundation area, stage, and discharge: a review. Hydrological Processes 11: 1427–1439.

    Google Scholar 

  • Stellingwerf, J.S., S. Phelan, F.M. Doohan, V. Ortiz, D. Griffin, A. Bourke, R.C.B. Hutten, D.E.L. Cooke, S. Kildea, and E. Mullins. 2018. Evidence for selection pressure from resistant potato genotypes but not from fungicide application within a clonal Phytophthora infestans population. Plant Pathology 67: 1528–1538.

    CAS  Google Scholar 

  • Stewart, G.W. 1987. Collinearity and least squares regression. Statistical Science 2: 68–100.

    Google Scholar 

  • Tiwari, J.K., S. Siddappa, B.P. Singh, S.K. Kaushik, S.K. Chakrabarti, V. Bhardwaj, P. Chandel, and P. Wehling. 2013. Molecular markers for late blight resistance breeding of potato: An update. Plant Breeding 132: 237–245.

    CAS  Google Scholar 

  • Tupin, F., ed. 2014. Remote sensing imagery. Digital signal and image processing series. London: ISTE/Wiley.

    Google Scholar 

  • Turkensteen, L.J., W.G. Flier, R. Wanningen, and A. Mulder. 2000. Production, survival and infectivity of oospores of Phytophthora infestans. Plant Pathology 49: 688–696.

    Google Scholar 

  • Xu, X.-M., and M.S. Ridout. 1998. Effects of initial epidemic conditions, sporulation rate, and spore dispersal gradient on the spatio-temporal dynamics of plant disease epidemics. Phytopathology 88: 1000–1012.

    CAS  PubMed  Google Scholar 

  • Zhang, M., Q. Zhihao, and X. Liu. 2005. Remote sensed spectral imagery to detect late blight in field tomatoes. Precision Agriculture 6: 489–508.

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge contributions from Dr. Xueying Wang and Dr. Richard Alldredge. The authors acknowledge PPNS #0777, Department of Plant Pathology, College of Agricultural, Human, and Natural Resource Sciences, Agricultural Research Center, Hatch Project No. WNP00678, Washington State University, Pullman, WA 99164-6430 USA; and financial support from the Washington Potato commission.

Author information

Authors and Affiliations

  1. Data Science and Analytics, Pacific Northwest National Laboratory, Richland, WA, USA

    D. H. Farber

  2. Department of Mathematics, Washington State University, Pullman, WA, USA

    C. Kogan

  3. Department of Plant Pathology, Washington State University, Pullman, WA, USA

    D. A. Johnson

Authors
  1. D. H. Farber
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Kogan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D. A. Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. H. Farber.

Electronic supplementary material

ESM 1

(PDF 335 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Farber, D.H., Kogan, C. & Johnson, D.A. Disease Gradients of Late Blight of Potato from Infrared Images of Commercial Fields. Am. J. Potato Res. 97, 347–359 (2020). https://doi.org/10.1007/s12230-020-09778-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12230-020-09778-0

Keywords

Search

Navigation