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Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves

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Abstract

Ten, widely-used vegetation indices (VIs), based on mathematical combinations of narrow-band optical reflectance measurements in the visible/near infrared wavelength range were evaluated for their ability to discriminate leaves of 1 month old wheat plants infected with yellow (stripe), leaf and stem rust. Narrow band indices representing changes in non-chlorophyll pigment concentration and the ratio of non-chlorophyll to chlorophyll pigments proved more reliable in discriminating rust infected leaves from healthy plant tissue. Yellow rust produced the strongest response in all the calculated indices when compared to healthy leaves. No single index was capable of discriminating all three rust species from each other. However the sequential application of the Anthocyanin Reflectance Index to separate healthy, yellow and mixed stem rust/leaf rust classes followed by the Transformed Chlorophyll Absorption and Reflectance Index to separate leaf and stem rust classes would provide for the required species discrimination under laboratory conditions and thus could form the basis of rust species discrimination in wheat under field conditions.

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References

  • Allen, R. (1928). A cytological study of Puccinia glumarum on Bromus marginatus and Triticum vulgare. Journal of Agricultural Research, 36, 487–513.

    Google Scholar 

  • Aparicio, N., Villegas, D., & Casadesus, J. (2000). Spectral vegetation indices as non-destructive tools for determining durum wheat yield. Agronomy Journal, 92, 83–91.

    Google Scholar 

  • Blackburn, G. A. (1998). Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. International Journal of Remote Sensing, 19(4), 657–675. doi:10.1080/014311698215919.

    Article  Google Scholar 

  • Bravo, C., Moshou, D., West, J., McCartney, A., & Ramon, H. (2003). Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering, 84(2), 137–145. doi:10.1016/S1537-5110(02)00269-6.

    Article  Google Scholar 

  • Bushnell, W. R. (1985). Structural and physiological alterations in susceptible host tissue. In A. P. Roelfs & W. R. Bushnell (Eds.), The cereal rusts, vol. 2, diseases, distribution, epidemiology, control (pp. 477–500). Orlando, FL, USA: Academic Press.

    Google Scholar 

  • Campbell, J. B. (1996). Introduction to remote sensing (2nd ed.). New York: The Guilford Press.

    Google Scholar 

  • Diker, K., & Bausch, W. C. (2003). Potential use of nitrogen reflectance index to estimate plant parameters and yield of maize. Biosystems Engineering, 85(4), 437–447. doi:10.1016/S1537-5110(03)00097-7.

    Article  Google Scholar 

  • Eversmeyer, M. G., & Kramer, C. L. (2000). Epidemiology of wheat leaf and stem rust in the central great plains of the USA. Annual review of Phytopathology, 38, 491–513. doi:10.1146/annurev.phyto.38.1.491.

    Article  PubMed  CAS  Google Scholar 

  • Filella, I., Serrano, L., Serra, J., & Penuelas, J. (1995). Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Science, 35, 1400–1405.

    Google Scholar 

  • Franke, J., Menz, G., Oerke, E.-C., & Rascher, U. (2005). Comparison of multi- and hyperspectral imaging data of leaf rust infected wheat plants. In G. D. U. Manfred Owe (Ed.), SPIE-volume 5976 remote sensing for agriculture, ecosystems, and hydrology VII p 59761D. Washington, USA: SPIE - The International Society for Optical Engineering.

  • Gamon, J. A., Penuelas, 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. doi:10.1016/0034-4257(92)90059-S.

    Article  Google Scholar 

  • 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(1), 38–45. doi:10.1562/0031-8655(2001)074<0038:OPANEO>2.0.CO;2.

    Article  PubMed  CAS  Google Scholar 

  • GRDC (2006). The rust diseases of winter cereals-diagnosis, epidemiology and determining economic thresholds. GRDC. Retrieved April 10, 2006 from http://www.grdc.com.au/growers/res_upd/south/s04s/wellings.htm.

  • Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2–3), 416–426. doi:10.1016/S0034-4257(02)00018-4.

    Article  Google Scholar 

  • Hansen, P. M., & Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 86(4), 542–553. doi:10.1016/S0034-4257(03)00131-7.

    Article  Google Scholar 

  • Lamb, D. W. (1999). Airborne digital imaging- the monitoring tool in conservation farming beyond 2000. In J. Kent (Ed.), 26th Riverina Outlook Conference, Australia: Wagga Wagga.

  • Lamb, D. W. (2000). The use of qualitative airborne multispectral imaging for managing agricultural crops-a case study in south-eastern Australia. Australian Journal of Experimental Agriculture, 40, 725–738. doi:10.1071/EA99086.

    Article  Google Scholar 

  • Lillesand, T. M., & Kiefer, R. W. (1994). Remote sensing and image interpretation (3rd ed.). New York, USA: John Wiley & Sons, Inc.

    Google Scholar 

  • 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. Physiologia Plantarum, 106, 135–141. doi:10.1034/j.1399-3054.1999.106119.x.

    Article  CAS  Google Scholar 

  • Moldenhauer, J., Moerschbacher, B. M., & van der Westhuizen, A. J. (2006). Histological investigation of stripe rust (Puccinia striiformis f.sp.tritici) development in resistant and susceptible wheat cultivars. Plant Pathology, 65, 469–474. doi:10.1111/j.1365-3059.2006.01385.x.

    Article  Google Scholar 

  • Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., McCartney, A., et al. (2005). Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real-Time Imaging, 11(2), 75–83. doi:10.1016/j.rti.2005.03.003.

    Article  Google Scholar 

  • Murray, G., Wellings, C., Simpfendorfer, S., & Cole, C. (2005). Stripe rust: Understanding the disease in wheat. New South Wales Department of Primary Industries. Retrieved April 10, 2006 from http://www.ricecrc.org/reader/winter-cereals/stripe-rust-in-wheat.pdf?MIvalObj=25431&doctype=document&MItypeObj=application/pdf&name=/stripe-rust-in-wheat.pdf.

  • Murray, G. M., & Brown, J. F. (1987). The incidence and relative importance of wheat diseases in Australia. Australasian Plant Pathology, 16(2), 34–37. doi:10.1071/APP9870034.

    Article  Google Scholar 

  • Myers, V. I. (1983). Remote sensing applications in agriculture. In N. R. Colwell, J. E. Estes, & G. A. Thorley (Eds.), Manual of remote sensing- second edition: Volume II-interpretation, applications (pp. 2111–2228). Virginia, USA: American Society of Photogrammetry.

    Google Scholar 

  • NSW DPI (2008). Winter crop variety sowing guide 2008-Part 2. NSW DPI. Retrieved April 28, 2008 from http://www.dpi.nsw.gov.au/__data/assets/pdf_file/0003/108633/wcvsg-Part2.pdf.

  • O’Connor. (2003). Cropping systems for enduring productivity in solutions for a better environment. In 11th Australian Agronomy Conference, Australian Society for Agronomy.

  • Parker, S. R., Shaw, M. W., & Royle, D. J. (1995). The reliability of visual estimates of disease severity on cereal leaves. Plant Pathology, 44, 856–864. doi:10.1111/j.1365-3059.1995.tb02745.x.

    Article  Google Scholar 

  • Penuelas, J., Baret, F., & Filella, I. (1995a). Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31, 221–230.

    CAS  Google Scholar 

  • Penuelas, J., Filella, I., & Gamon, J. A. (1995b). Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytologist, 131, 291–296. doi:10.1111/j.1469-8137.1995.tb03064.x.

    Article  Google Scholar 

  • Penuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J., & Field, C. B. (1994). Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of Environment, 48(2), 135–146. doi:10.1016/0034-4257(94)90136-8.

    Article  Google Scholar 

  • Perry, C. R., & Lautenschlager, L. F. (1984). Functional equivalence of spectral vegetation indices. Remote Sensing of Environment, 14, 169–182. doi:10.1016/0034-4257(84)90013-0.

    Article  Google Scholar 

  • Qin, Z., & Zhang, M. (2005). Detection of rice sheath blight for in-season disease management using multispectral remote sensing. International Journal of Applied Earth Observation and Geoinformation, 7(2), 115–128. doi:10.1016/j.jag.2005.03.004.

    Article  Google Scholar 

  • Roelfs, A. P. (1985). Wheat and rye stem rust. In A. P. Roelfs & W. R. Bushnell (Eds.), The cereal rusts, vol. 2, diseases, distribution, epidemiology, control (pp. 4–33). Orlando, FL, USA: Academic Press.

    Google Scholar 

  • Saari, E. E., & Prescott, J. M. (1985). World distribution in relation to economic losses. In A. P. Roelfs & W. R. Bushnell (Eds.), The cereal rusts, vol. 2, diseases, distribution, epidemiology, control (pp. 259–298). Orlando, FL, USA: Academic Press.

    Google Scholar 

  • Samborski, D. J. (1985). Wheat leaf rust. In A. P. Roelfs & W. R. Bushnell (Eds.), The cereal rusts, vol. 2, diseases, distribution, epidemiology, control (pp. 39–55). Orlando, FL, USA: Academic Press.

    Google Scholar 

  • Sharp, E. L., Perry, C. R., Scharen, A. L., Boatwright, G., Sands, D. C., Lautenschlager, L. F., et al. (1985). Monitoring cereal rust development with a special radiometer. Phytopathology, 75, 936–939. doi:10.1094/Phyto-75-936.

    Article  Google Scholar 

  • Singh, R. P., Huerta-Espino, J., & Roelfs, A. P. (2002). The wheat rusts. In B. C. Curtis, S. Rajaram, and H.G. Macherson (Eds.), Bread wheat-improvement and production vol. 30. Rome: Plant Production and Protection Series, Food and Agriculture Organisation of the United Nations. Retrieved October 30, 2008 from http://www.fao.org/documents/show_cdr.asp?url_file=/docrep/006/y4011e/y4011e00.HTM.

  • 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(2), 153–158. doi:10.1094/PD-89-0153.

    Article  Google Scholar 

  • Tarpley, L., Reddy, K., & Sassenrath-Cole, F. (2000). Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration. Crop Science, 40, 1814–1819.

    Article  Google Scholar 

  • Tartachnyk, I. I., Rademacher, I., & Kühbauch, W. (2006). Distinguishing nitrogen deficiency and fungal infection of winter wheat by laser-induced fluorescence. Precision Agriculture, 7(4), 281–293. doi:10.1007/s11119-006-9008-7.

    Article  Google Scholar 

  • Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71(2), 158–182. doi:10.1016/S0034-4257(99)00067-X.

    Article  Google Scholar 

  • Trotter, G. M., Whitehead, D., & Pinkney, E. J. (2002). The photochemical reflectance index as a measure of photosynthetic light use efficiency for plants of varying foliar nitrogen contents. International Journal of Remote Sensing, 23(6), 1207–1212. doi:10.1080/01431160110106096.

    Article  Google Scholar 

  • Tucker, C. J. (1979). Red and photographic infrared linear combination for monitoring vegetation. Remote Sensing of Environment, 8, 127–150. doi:10.1016/0034-4257(79)90013-0.

    Article  Google Scholar 

  • Tucker, C. J., Holben, B. N., Elgin, J., James, H., McMurtrey, I., & James, E. (1981). Remote sensing of total dry-matter accumulation in winter wheat. Remote Sensing of Environment, 11, 171–189.

    Article  Google Scholar 

  • USDA (2006). Importance of cereal rust disease in American agriculture. Cereal Disease Laboratory, Agricultural Research Service, United States Department of Agriculture. Retrieved March 25, 2006 from http://www.ars.usda.gov/Main/docs.htm?docid=9854.

  • Watkins, J. E. (2006). Leaf, stem and stripe rust diseases of wheat. Neb Guide: University of Nebraska-Lincoln. Retrieved March 23, 2006 from http://elkhorn.unl.edu/epublic/pages/publicationD.jsp?publicationId=310#top.

  • Wellings, C. R., & Kandel, K. R. (2004). Pathogen dynamics associated with historic stripe (yellow) rust epidemics in Australia in 2002 and 2003. In 11th International Cereal Rusts and Powdery Mildews Conference, John Innes Centre, Norwich, UK. European and Mediterranean Cereal Rust Foundation, Wageningen, Netherlands-2004. Cereal Rusts and Powdery Mildews Bulletin, Abstr. A2.74.

  • 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, 4, 593–614. doi:10.1146/annurev.phyto.41.121702.103726.

    Article  CAS  Google Scholar 

  • Young, A., & Britton, G. (1990). Carotenoids and stress. In R. G. Alscher & J. R. Cumming (Eds.), Stress responses in plants: Adaptation, acclimation mechanisms (pp. 87–112). New York: Wiley.

    Google Scholar 

  • Zadoks, J. C., Chang, T. T., & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research, 14, 415–421. doi:10.1111/j.1365-3180.1974.tb01084.x.

    Article  Google Scholar 

  • Zhang, M., Qin, Z., Liu, X., & Ustin, S. L. (2003). Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. International Journal of Applied Earth Observation and Geoinformation, 4(4), 295–310. doi:10.1016/S0303-2434(03)00008-4.

    Article  Google Scholar 

  • Zhao, D. H., Li, J. L., & Qi, J. G. (2005a). Identification of red and NIR spectral regions and vegetative indices for discrimination of cotton nitrogen stress and growth stage. Computers and Electronics in Agriculture, 48(2), 155–169. doi:10.1016/j.compag.2005.03.003.

    Article  Google Scholar 

  • Zhao, M., Heinsch, F. A., Nemani, R. R., & Running, S. W. (2005b). Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment, 95(2), 164–176. doi:10.1016/j.rse.2004.12.011.

    Article  Google Scholar 

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Acknowledgement

This work was partly conducted within the CRC for Spatial Information (CRCSI), established and supported under the Australian Governments Cooperative Research Centres Programme. The authors gratefully acknowledge Prof. Robert Park (University of Sydney, Cereal Rust Laboratory, Cobbitty, NSW Australia) for provision of the laboratory and plant material used for collection of spectral data, Mr. Graham Hyde (UNE Physics Technical Officer) for ongoing technical support and staff of UNE’s Science and Engineering Workshop (SEW) for construction of the leaf reflectance spectrometer. One author (RD) gratefully acknowledges the receipt of Postgraduate Funding (RD) from the University of New England (UNE) and a ‘Top-up’ Postgraduate Research Scholarship from the CRCSI.

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Devadas, R., Lamb, D.W., Simpfendorfer, S. et al. Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agric 10, 459–470 (2009). https://doi.org/10.1007/s11119-008-9100-2

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