Spectral signatures of sugar beet leaves for the detection and differentiation of diseases

Precision Agriculture

Volumn 11, Issue 4, 2010, Pages 413-431

  Mahlein, A K ^a   Steiner, U ^a   Dehne, H W ^a   Oerke, E C ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

Cercospora beticola; Disease detection; Erysiphe betae; Hyperspectral; Remote sensing; Spectral signature; Sugar beet; Uromyces betae; Vegetation indices

Indexed keywords

DATA PROCESSING; DISEASE SEVERITY; FUNGAL DISEASE; NDVI; PHYSIOLOGICAL RESPONSE; RADIOMETER; SPECTRAL REFLECTANCE; SUGAR BEET; WAVELENGTH;

BETA VULGARIS SUBSP. VULGARIS; CERCOSPORA; CERCOSPORA BETICOLA; ERYSIPHALES; ERYSIPHE BETAE; UROMYCES;

EID: 77954143049     PISSN: 13852256     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11119-010-9180-7     Document Type: Article

References (46)

1. Blackburn, G. A. (1998). Quantifying chlorophylls and carotenoids at leaf and canopy scale: An evaluation of some hyperspectral approaches. Remote Sensing of the Environment, 66, 273-285.
2. Blackburn, G. A. (2007). Hyperspectral remote sensing of plant pigments. Journal of Experimental Botany, 58, 844-867.
3. Bravo, C., Moushou, D., West, J., McCartney, A., & Ramon, H. (2003). Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering, 84, 137-145.
4. Carter, G. A., & Knapp, A. K. (2001). Leaf optical properties in higher plants: Linking spectral characteristics to stress and chlorophyll concentration. American Journal of Botany, 88, 677-684.
5. 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.
6. Delalieux, S., Somers, B., Verstaeten, 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.
7. Delalieux, S., van Aardt, J., Kelemans, W., & 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.
8. Francis, S. (2002). Sugar-beet powdery mildew (Erysiphe betae). Molecular Plant Pathology, 3, 119-124.
9. Gamon, J. A., & Surfus, J. S. (1999). Assessing leaf pigment content and activity with a reflectometer. New Phytologist, 143, 105-117.
10. Gitelson, A. A., Gritz, Y., & Merzylak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160, 271-282.
11. Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of the Environment, 80, 76-87.
12. Gitelson, A. A., Merzlyak, M. N., & Chivkunova, O. B. (2001). Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology, 74, 38-45.
13. Guyot, G., & Baret, F. (1988). Utilisation de la haute résolution spectrale pour suivre l'état des couverts végétaux [Utilisation of high spectral resolution for monitoring vegetation condition]. In T. D. Guyenne & J. J. Hunt (Eds.), Proceedings of 4th international colloquim spectral signatures of objects in remote sensing, proceedings of the conference held 18-22 January 1988 in Aussois (Modane), France (pp. 279-286). ESA SP-287. European Space Agency.
14. Hatfield, J. L., Gitelson, A. A., Schepers, J. S., & Walthall, C. L. (2008). Application of spectral remote sensing for agronomic decisions. Agronomy Journal, 100, 117-131.
15. Heath, M. C. (1997). Signalling between pathogenic rust fungi and resistant of susceptible host plants. Annals of Botany, 80, 713-720.
16. Hillnhuetter, C., & Mahlein, A.-K. (2008). Early detection and localisation of sugar beet diseases: New approaches. Gesunde Pflanzen, 60(4), 143-149.
17. Jacquemoud, S., & Baret, F. (1990). PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment, 34, 75-91.
18. Jing, L., Jinbao, J., Yunhao, C., Yuanyuan, W., Wei, S., & Wenjiang, H. (2007). Using hyperspectral indices to estimate foliar chlorophyll a concentrations of winter wheat under yellow rust stress. New Zealand Journal of Agricultural Research, 50, 1031-1036.
19. Jones, H. G., Archer, N., Rotenburg, E., & Casa, R. (2003). Radiation measurement for plant ecophysiology. Journal of Experimental Botany, 54, 879-889.
20. Jones, J. D. G., & Dangl, J. L. (2006). The plant immune system. Nature, 444, 323-329.
21. Knogge, W. (1996). Fungal infections of plants. The Plant Cell, 8, 1711-1722.
22. Kobayashi, T., Kanda, E., Kitada, K., Ishiguro, K., & Torigoe, Y. (2001). Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. Phytopathology, 91, 316-323.
23. Larsolle, A., & Hamid Muhammed, H. (2007). Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density. Precision Agriculture, 8, 37-47.
24. Laudien, R. (2005). Entwicklung eines GIS-gestützten schlagbezogenen Führungsinformationssystems für die Zuckerwirtschaft. [Development of a field- and GIS-based management information system for the sugar beet industry] PhD thesis, University of Hohenheim.
25. Le Maire, G., Francois, C., & Dufrene, E. (2004). Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment, 89, 1-28.
26. Mahlein, A.-K., Hillnhütter, C., Mewes, T., Scholz, C., Steiner, U., Dehne, H.-W., & Oerke, E.-C. (2009). Disease detection in sugar beet fields: A multi-temporal and multi-sensoral approach on different scales. In C. M. U. Neale & A. Maltese (Eds.), Proceedings of the SPIE Europe conference on remote sensing (Vol. 7472, pp. 747228-747228-10).
27. Malthus, T. J., & Madeira, A. C. (1993). High resolution spectroradiometry: Spectral reflectance of field bean leaves infected by Botrytis fabae. Remote Sensing of Environment, 45, 107-116.
28. Meier, U., Bachmann, L., Buhtz, H., Hack, H., Klose, R., Märländer, B., et al. (1993). Phänologische Entwick-lungsstadien der Beta-Rüben (Beta vulgaris L. ssp.). Codierung und Beschreibung nach der erweiterten BBCH-Skala (mit Abbildungen). [Phenological growth stages of sugar beet (Beta vulgaris L. ssp.) Codification and description according to the general BBCH scale (with figures).]. Nachrichtenbl Deut Pflanzenschutzd, 45, 37-41.
29. Mendgen, K., & Hahn, M. (2002). Plant infection and the establishment of fungal biotrophy. Trends in Plant Science, 7, 352-356.
30. Moran, M. S., Inoue, Y., & Barnes, E. M. (1997). Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of the Environment, 61, 319-346.
31. Naidu, R. A., Perry, E. M., Pierce, F. J., & Mekuria, T. (2009). The potential of spectral reflectance technique for the detection of grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Computers and Electronics in Agriculture, 66, 38-45.
32. Nilsson, H.-E. (1995). Remote sensing and image analysis in plant pathology. Annual Review of Phytopathology, 15, 489-527.
33. Nutter, F. W., Jr., Littrell, R. H., & Brennemann, T. B. (1990). Utilization of a multispectral radiometer to evaluate fungicide efficacy to control late leaf spot in peanut. Phytopathology, 80, 102-108.
34. Penuelas, J., Baret, F., & Filella, I. (1995). Semiempirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31, 221-230.
35. Pietrzykowski, E., Stone, C., Pinkard, E., & Mohammed, C. (2006). Effects of Mycosphaerella leaf disease on the spectral reflectance properties of juvenile Eucalyptus globules foliage. Forest Pathology, 36, 334-348.
36. Pinter, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daugthry, C. S. T., et al. (2003). Remote sensing for crop management. Photogrammetric Engineering and Remote Sensing, 69, 647-664.
37. Richardson, A. D., Duigan, S. P., & Berlyn, G. P. (2001). An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytologist, 153, 185-194.
38. Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of the third earth resources technology satellite-1 symposium (pp. 301-317). Greenbelt, MD: NASA.
39. Stafford, J. V. (2000). Implementing precision agriculture in the 21st century. Journal Agricultural Engineering Research, 76, 267-275.
40. Steddom, K., Bredehoeft, M. W., Khan, M., & Rush, C. M. (2005). Comparison of visual and multispectral radiometric disease evaluations of Cercospora leaf spot of sugar beet. Plant Disease, 89, 153-158.
41. Steddom, K., Heidel, G., Jones, D., & Rush, C. M. (2003). Remote detection of rhizomania in sugar beets. Phytopathology, 93, 720-726.
42. Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Hyperspectral vegetation indices and their relationship with agricultural crop characteristics. Remote Sensing of Environment, 71, 158-182.
43. van Kan, J. A. L. (2006). Licensed to kill: The lifestyle of a necrotrophic plant pathogen. Trends in Plant Science, 11, 247-253.
44. West, J. S., Bravo, C., Oberti, R., Lemaire, D., Moshou, D., & McCartney, H. A. (2003). The potential of optical canopy measurement for targeted control of field crop diseases. Annual Review of Phytopathology, 41, 593-614.
45. Wolf, P. F. J., & Verreet, J. A. (2002). The IPM sugar beet model, an integrated pest management system in Germany for the control of fungal leaf diseases in sugar beet. Plant Diseases, 86, 336-344.
46. Zhang, M., Liu, X., & O'Neill, M. (2002). Spectral discrimination of Phytophthora infestans infections on tomatoes based on principal component and cluster analyses. International Journal of Remote Sensing, 23, 1095-1107.