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Detection of diseased rubber plantations using satellite remote sensing

  • B. K. Ranganath
  • N. Pradeep
  • V. B. Manjula
  • Balakrishna Gowda
  • M. D. Rajanna
  • Damodar Shettigar
  • P. P. Nageswara RAo
Cover Article

ABSTRACT

The study evaluates the potential of satellite remote sensing technology for detection, mapping and monitoring of diseased rubber plantation affected by Corynespora and Gloeosporium fungi, which causes leaf spot and leaf fall. Multi-date satellite data of IRS-1C have been analyzed adopting enhancement and classification techniques to identify and extract information on the spatial extent and distribution of healthy and diseased rubber plants with an accuracy of 90%. The diseased rubber plantations have shown considerable reduction in the near-infrared reflectance followed by a rise in the reflectance in red and short wave infrared. Vegetation index images generated for different periods have shown the progress of disease incidence, severity and recovery of rubber plantations after fungicidal spraying. The study has demonstrated the use of remote sensing technology in identifying and delineating diseased rubber plantations. Early detection of the disease would be of immense value for taking up necessary control measures and minimize the loss.

Keywords

Rubber Normalize Difference Vegetation Index Remote Sensing Vegetation Index Natural Rubber 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Carter, G. A. (1994). Ratios of leaf reflectance in narrow spectral bands as indicators of plant stress. Int. J. Remote Sensing,15: 697–703.CrossRefGoogle Scholar
  2. Edathil, T.T., Jacob, C.K. and Annakutty Joseph (2000). Leaf Diseases. In: Natural Rubber-Agro-management and Crop Processing (Eds. P.J. George and C. Kuruvilla Jacob). Rubber Research Institute of India (RRII), Kerala, pp. 583–598.Google Scholar
  3. George, N.K. and Edathil, T.T. (1988). A report on Corynespora leaf spot disease on mature rubber. International Rubber Conference, Kottayam, Kerala.Google Scholar
  4. George, P.J. and Panikkar, A.O.N. (2000). Rubber yielding plants. In: Natural Rubber-Agro-management and Crop Processing (Eds. P.J. George and C. Kuruvilla Jacob).Rubber Research Institute of India (RRII), Kerala, pp. 20–28.Google Scholar
  5. James, E.V. and Barret, N.R (1989). Use of Thematic Mapper Data for the detection of Forest damage caused by the pear thrips. Remote Sensing Env.,30: 217–225.CrossRefGoogle Scholar
  6. Jensen, JR. (1996). Introductory Digital Image Processing - A Remote Sensing Perspective, (Second Edition), Prentice Hall, New York, USA.Google Scholar
  7. Joshi, P.K., Sarnam Singh, Shefali Aggarwal and. Roy P.S. (2001). Land use and land cover classification in Jammu and Kashmir using WiFS data. Current Science,81(4): 392–399.Google Scholar
  8. Kanemasu, E.T., Niblett, C.L., Manges, H., Lenhert, D. and Newman, M.A. (1974). Wheat – its growth and disease severity as deduced from ERTS-1, Remote Sensing Env.,3: 255–260.CrossRefGoogle Scholar
  9. Kuruvilla, J.C., (1977). Diseases of potential threat to rubber in India. The Planters Chronicle.92(10): 451–461.Google Scholar
  10. Lalithakumari, K. and Jacob, J. (2000). Natural rubber industry in India. An overview. In: Natural Rubber-Agromanagement and Crop Processing (Eds. P.J. George and C. Kuruvilla Jacob). Rubber Research Institute of India (RRII), Kerala, pp. 583–598.Google Scholar
  11. Lillesand, T.M. and Kieffer, R.W. (1999). Remote Sensing and Image Interpretation, John Wiley and Sons, New York, USA.Google Scholar
  12. Manju, M.J., Sabu P. Idicula, Joseph, A., Joy. M. and Kothandaraman R. (1999). Incidence and severity of Gloeosporium leaf disease of rubber in South India. Indian J. of Natural Rubber Res.,12(1&2): 34–38.Google Scholar
  13. Mukai, Y, Sugimora, T., Watanabe, H. and Wakamori, K. (1987). Extraction of areas infested by Pine bark beetle using Landsat MSS data. Photogramm. Engg. & Remote Sensing,53: 77–81.Google Scholar
  14. Nelson, R.F. (1983). Detecting Forest Canopy due to insect activity using Landsat MSS. Photogramm. Engg. & Remote Sensing,49: 1303–1314.Google Scholar
  15. Nilsson, E.H. (1995). Remote sensing and image analysis in plant pathology. Ann. Rev. of Phytopathology,15: 489–527.CrossRefGoogle Scholar
  16. Rajalakshmy, V.K. and Kothandaraman, R. (1996). Current status of Corynespora leaf fall in India- the occurrence and management. Proc. Workshop on Corynespora leaf fall disease of Heavea Rubber, held at Medan, Indonesia, pp. 37–43.Google Scholar
  17. Ramakrishnan, T.S. and Pillai, P.N.R. (1961). Occurrence of Corynespora leaf disease in nursery seedling. Rubber Board Bulletin,5(1): 32–35.Google Scholar
  18. Rao, P.P.N. and Rao, V.R. (1982). Identification of Brown Plant Hopper and Bacterial Leaf Blight affected rice crop on Landsat false colour composites. Proc. of 3rd Asian Conf. Remote Sensing, held at Dhaka, Bangladesh from Dec. 4-7, 1982, pp. 1-12.Google Scholar
  19. Rao, P.P.N., Jayaraman, V. and Chandrashekar M. G. (1991). Application of remote sensing in plant protection, ISRO Technical Report, ISRO-NNRMS-TR-89-91:5-20.Google Scholar
  20. Ray, D.J. (1986). Remote sensing of biotic and abiotic plant stress. Ann. Rev. Phytopathology.24: 265–287.CrossRefGoogle Scholar
  21. Venette, J.R. and Venette, R.C. (1991). Image analysis for evaluation of Bean Rust severity. Phytopathology,81: 1213–1225.Google Scholar

Copyright information

© Springer 2004

Authors and Affiliations

  • B. K. Ranganath
    • 1
  • N. Pradeep
    • 1
  • V. B. Manjula
    • 1
  • Balakrishna Gowda
    • 2
  • M. D. Rajanna
    • 2
  • Damodar Shettigar
    • 3
  • P. P. Nageswara RAo
    • 1
  1. 1.Regional Remote Sensing Service CentreIndian Space Research OrganisationBangaloreIndia
  2. 2.University of Agricultural SciencesGandhi Krishi Vignana Kendra CampusBangaloreIndia
  3. 3.Karnataka Forest Development CorporationGovernment of KarnatakaSulliaIndia

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