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Precision Agriculture

, Volume 12, Issue 5, pp 699–715 | Cite as

Thermographic assessment of scab disease on apple leaves

  • E.-C. Oerke
  • P. Fröhling
  • U. Steiner
Article

Abstract

Phytopathogenic fungi may affect both the cuticular and the stomatal conductance of plant tissue resulting in significant modifications of leaf temperature. Venturia inaequalis colonizes apple leaves below the cuticle (subcuticularly) causing scab disease. The suitability of digital infrared thermography for sensing and quantifying apple scab was assessed by investigating the effects of V. inaequalis on the water balance of apple leaves in relation to the disease stage and the severity of scab. Transpiration was measured by infrared thermo-imaging to evaluate spatial heterogeneity of the leaves in response to localized infections. Fungal development was assessed microscopically. Subcuticular growth of the pathogen caused localized decreases in leaf temperature before symptoms appeared that significantly increased the maximum temperature difference (MTD) of leaves. The MTD increased with scab development and was strongly correlated to the size of infection sites (r²linear = 0.85) and overall disease severity (% diseased leaf area, r²square = 0.71). In later stages of the disease, the MTD decreased because of leaf senescence. Thermographic measurements revealed differences in disease severity resulting from disease stage, resistance of host tissue and differences in the aggressiveness of V. inaequalis isolates. Subcuticular growth of the pathogen was beyond the area of conidia production, therefore, the area of leaf with increased transpiration was larger than the scab lesions; the proportion decreased from >70% in the early stages to <20% for mature lesions. Leaf transpiration was increased by all stages of scab development, therefore, MTD may be used not only for the differentiation between diseased and non-diseased leaves, but also for disease quantification, e.g. in screening systems and monitoring in precision agriculture.

Keywords

Apple scab Remote sensing Disease quantification Thermal imaging Transpiration rate Venturia inaequalis 

Notes

Acknowledgments

We would like to thank Phil E. Russell for critical reading of a former version of the manuscript. We are grateful to Alexander Prange, Hochschule Niederrhein, Mönchengladbach (D) for providing the AquaLab CX-3.

References

  1. Aderhold, R. (1896). Die Fusicladien unserer Obstbäume, 1. Teil. Diels Landwirtschaftliches Jahrbuch, 25, 875–914.Google Scholar
  2. Allegre, M., Daire, X., Heloir, M. C., Trouvelot, S., Mercier, L., Adrian, M., et al. (2007). Stomatal deregulation in Plasmopara viticola-infected grapevine leaves. New Phytologist, 173, 832–840.PubMedCrossRefGoogle Scholar
  3. Ayres, P. G., & Jones, P. (1975). Increased transpiration and the accumulation of root absorbed 86Rb in barley leaves infected by Rhynchosporium secalis (leaf blotch). Physiological Plant Pathology, 7, 49–58.CrossRefGoogle Scholar
  4. Bassanezi, R. B., Amorim, L., Bergamin, F. A., & Berger, R. D. (2002). Gas exchange and emission of chlorophyll fluorescence during the monocycle of rust, angular leaf spot and anthracnose on bean leaves as a function of their trophic characteristics. Journal of Phytopathology, 150, 37–47.CrossRefGoogle Scholar
  5. Chaerle, L., Van Caeneghem, W., Messens, E., Lambers, H., van Montagu, M., & van der Straeten, D. (1999). Presymptomatic visualization of plant–virus interactions by thermography. Nature Biotechnology, 17, 813–816.PubMedCrossRefGoogle Scholar
  6. Chaerle, L., de Boever, F., van Montagu, M., & van der Straeten, D. (2001). Thermographic visualization of cell death in tobacco and Arabidopsis. Plant, Cell and Environment, 24, 15–25.CrossRefGoogle Scholar
  7. Chaerle, L., Hagenbeek, D., de Bruyne, E., Valcke, R., & van der Straeten, D. (2004). Thermal and chlorophyll-fluorescence imaging distinguish plant–pathogen interactions at an early stage. Plant Cell Physiology, 45, 887–896.PubMedCrossRefGoogle Scholar
  8. Corlett, M., Chong, J., & Kokko, E. G. (1976). The ultrastructure of the Spilocea state of Venturia inaequalis in vivo. Canadian Journal of Microbiology, 22, 1144–1152.PubMedCrossRefGoogle Scholar
  9. Delalieux, S., van Aardt, J., Keulemans, W., Schrevens, E., & 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, 130–143.CrossRefGoogle Scholar
  10. 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
  11. Di Giorgio, D., Camoni, L., Mott, K. A., Takemoto, J. Y., & Ballio, A. (1996). Syringopeptins, Pseudomonas syringae pv. syringae phytotoxins, resemble syringomycin in closing stomata. Plant Pathology, 45, 564–571.CrossRefGoogle Scholar
  12. Fito, P. J., Ortola, M. D., de los Reyes, D., Fito, P., & de los Reyes, E. (2004). Control of citrus surface drying by image analysis of infrared thermography. Journal of Food Engineering, 61, 287–290.CrossRefGoogle Scholar
  13. Gessler, C., & Stumm, D. (1984). Infection and stroma formation by Venturia inaequalis on apple leaves with different degrees of susceptibility to scab. Phytopathologische Zeitschrift, 110, 119–126.CrossRefGoogle Scholar
  14. Hignett, R. C., & Kirkham, D. S. (1967). The role of extracellular melanoproteins in Venturia inaequalis in host susceptibility. Journal of General Microbiology, 48, 269–275.PubMedGoogle Scholar
  15. Inoue, Y., Kimball, B. A., Jackson, R. D., Pinter, P. J., & Reginato, R. J. (1990). Remote estimation of leaf transpiration rate and stomatal resistance based on infrared thermometry. Agricultural and Forest Meteorology, 51, 21–33.CrossRefGoogle Scholar
  16. Jones, H. G. (1992). Plant and microclimate (2nd ed.). Cambridge, UK: Cambridge University Press.Google Scholar
  17. Jones, H. G. (2004). Application of thermal imaging and infrared sensing in plant physiology and ecophysiology. Advances in Botanical Research, 41, 107–163.CrossRefGoogle Scholar
  18. Jones, H. G., Stoll, M., Santoa, T., de Sousa, C., Chaves, M. M., & Grant, O. M. (2002). Use of infrared thermography for monitoring stomatal closure in the field: Application to grapevine. Journal of Experimental Botany, 53, 2249–2260.PubMedCrossRefGoogle Scholar
  19. Kümmerlen, B., Dauwe, S., Schmundt, D., & Schurr, U. (1999). Thermography to measure water relations of plant leaves. In B. Jähne (Ed.), Handbook of computer vision and applications (Vol. 3, pp. 763–781). London: Academic Press.Google Scholar
  20. Leinonen, I., & Jones, H. G. (2004). Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. Journal of Experimental Botany, 55, 1423–1431.PubMedCrossRefGoogle Scholar
  21. Leinonen, I., Grant, O. M., Tagliavia, C. P. P., Chaves, M. M., & Jones, H. G. (2006). Estimating stomatal conductance with thermal imagery. Plant, Cell and Environment, 29, 1508–1518.PubMedCrossRefGoogle Scholar
  22. Lenthe, J.-H., Oerke, E.-C., & Dehne, H.-W. (2007). Digital infrared thermography for monitoring canopy health of wheat. Precision Agriculture, 8, 15–26.CrossRefGoogle Scholar
  23. Lindenthal, M., Steiner, U., Dehne, H.-W., & Oerke, E.-C. (2005). Effect of downy mildew development on transpiration of cucumber leaves visualized by digital infrared thermography. Phytopathology, 95, 233–240.PubMedCrossRefGoogle Scholar
  24. Luquet, D., Begue, A., Vidal, A., Clouvel, P., Dauzat, J., et al. (2003). Using multidirectional thermography to characterize water status of cotton. Remote Sensing of Environment, 84, 411–421.CrossRefGoogle Scholar
  25. MacHardy, W. E. (1995). Apple scab: Biology, epidemiology, and management. St. Paul, MN: APS Press.Google Scholar
  26. MacHardy, W. E., & Gadoury, D. M. (1989). A revision of Mills’s criteria for predicting apple scab infection periods. Phytopathology, 79, 304–310.CrossRefGoogle Scholar
  27. McDonald, K. L., & Cahill, D. M. (1999). Evidence for a transmissible factor that causes rapid stomatal closure in soybean at sites adjacent to and remote from hypersensitive cell death induced by Phytophthora sojae. Physiological and Molecular Plant Pathology, 55, 197–203.CrossRefGoogle Scholar
  28. Merlot, S., Mustilli, A. C., Genty, B., North, H., Lefebre, V., et al. (2002). Use of infrared thermal imaging to isolate Arabidopsis mutants defective in stomatal regulation. Plant Journal, 30, 601–609.PubMedCrossRefGoogle Scholar
  29. Mills, W. D. (1931). A method of detecting and demonstrating early leaf infections of apple scab. Phytopathology, 21, 338–339.Google Scholar
  30. Möller, M., Alchanatis, V., Cohen, Y., Tsipris, J., Naor, A., et al. (2007). Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. Journal of Experimental Botany, 58, 827–838.PubMedCrossRefGoogle Scholar
  31. Nusbaum, C. J., & Keitt, G. W. (1938). A cytological study of host-parasite relations of Venturia inaequalis on apple leaves. Journal of Agricultural Research (Washington), 56, 595–618.Google Scholar
  32. Oerke, E.-C., Lindenthal, M., Fröhling, P., & Steiner, U. (2005). Digital infrared thermography for the assessment of leaf pathogens. In J. V. Stafford (Ed.), Precision agriculture ‘05 (pp. 91–98). Wageningen: Wageningen University Press.Google Scholar
  33. Oerke, E.-C., Steiner, U., Dehne, H.-W., & Lindenthal, M. (2006). Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions. Journal of Experimental Botany, 57, 2121–2132.PubMedCrossRefGoogle Scholar
  34. Pearce, R. S., & Fuller, M. P. (2001). Freezing of barley studied by infrared video thermography. Plant Physiology, 125, 227–240.PubMedCrossRefGoogle Scholar
  35. Prytz, G., Futsaether, C. M., & Johnsson, A. (2003). Thermography studies of the spatial and temporal variability in stomatal conductance of Avena leaves during stable and oscillatory transpiration. New Phytologist, 158, 258–259.Google Scholar
  36. Schönherr, J. (1982). Resistance of plant surfaces to water loss: Transport properties of cutin, suberin and associated lipids. In Encyclopedia of plant physiology (Vol. 12B, pp. 153-179). Berlin: Springer-Verlag.Google Scholar
  37. Shear, G. M., & Drake, C. R. (1971). Calcium accumulation in apple fruit infected with Venturia inaequalis (Cooke) Wint. Physiological Plant Pathology, 1, 313.CrossRefGoogle Scholar
  38. Smith, R. C. G., Heritage, A. D., Stapper, M., & Barrs, H. D. (1986). Effect of stripe rust (Puccinia striiformis West.) and irrigation on the yield and foliage temperature of wheat. Field Crops Research, 14, 39–51.CrossRefGoogle Scholar
  39. Stadelmann, F. X., & Schwinn, F. J. (1982). Contribution to the biology of Venturia inaequalis. Zeitschrift für Pflanzenkrankheiten und Pflanzenschutz, 89, 96–109.Google Scholar
  40. Stajnko, D., Lakota, M., & Hoevar, M. (2004). Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging. Computers and Electronics in Agriculture, 42, 31–42.CrossRefGoogle Scholar
  41. Stenzel, I., Steiner, U., Dehne, H.-W., & Oerke, E.-C. (2007). Occurrence of fungal leaf pathogens in sugar beet fields monitored with digital infrared thermography. In J. V. Stafford (Ed.), Precision agriculture ‘07 (pp. 529–535). Wageningen: Wageningen Academic Publishers.Google Scholar
  42. Stier, J. C., Filiault, D. L., Wisniewski, M., & Palta, J. P. (2003). Visualization of freezing progression in turfgrasses using infrared video thermography. Crop Science, 43, 415–420.CrossRefGoogle Scholar
  43. Stoll, M., Schultz, H. R., Baecker, G., & Berkelmann-Loehnertz, B. (2008a). Early pathogen detection under different water status and the assessment of spray application in vineyards through the use of thermal imagery. Precision Agriculture, 9, 407–417.CrossRefGoogle Scholar
  44. Stoll, M., Schultz, H. R., & Berkelmann-Loehnertz, B. (2008b). Exploring the sensitivity of thermal imaging for Plasmopara viticola pathogen detection in grapevines under different water status. Functional Plant Biology, 35, 281–288.CrossRefGoogle Scholar
  45. Wang, Y., Holroyd, G., Hetherington, A. M., & Ng, C. K. Y. (2004). Seeing ‘cool’ and ‘hot’-infrared thermography as a tool for non-invasive, high-throughput screening of Arabidopsis guard cell signaling mutants. Journal of Experimental Botany, 55, 1187–1193.PubMedCrossRefGoogle Scholar
  46. Wright, K. N., Duncan, G. H., Pradel, K. S., Carr, F., Wood, S., et al. (2000). Analysis of the N gene hypersensitive response induced by a fluorescently tagged tobacco mosaic virus. Plant Physiology, 123, 1375–1385.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Institute of Crop Science and Resource Conservation (INRES)—PhytomedicineUniversity of BonnBonnGermany
  2. 2.BASF AGLimburgerhofGermany

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