Precision Agriculture

, Volume 6, Issue 6, pp 489–508 | Cite as

Remote Sensed Spectral Imagery to Detect Late Blight in Field Tomatoes

  • Minghua Zhang
  • Zhihao Qin
  • Xue Liu


Late blight, caused by the fungal pathogen Phytophthora infestans, is a disease that quickly spreads in tomato fields under suitable weather conditions and can threaten the sustainability of tomato farming in California, USA. This paper explores the applicability of remotely sensed images to detect disease spectral anomalies for precision disease management. We used the indices approach and generated a 5-index image that we used to identify the disease in tomato fields based on information from field-collected spectra and linear combinations of the spectral indices. Field results indicated that we were able to identify five clusters in the image space with small overlaps of a few clusters. Using the identified 5-cluster scheme to classify the tomato field images, we were able to successfully separate the diseased tomatoes from the healthy ones before economic damage was caused. Hence, the method based on a 5-index image may significantly enhance the capability of multispectral remote sensing for disease discrimination at the field level.


remote sensing late blight plant disease near infrared image feature space visualization analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Agrios, G. N. 1997Plant Pathology4Academic PressLondon, UK653Google Scholar
  2. Bausch, W. C. 1993Soil background effects on reflectance-based crop coefficients for cornRemote Sensing of Environment46213222CrossRefGoogle Scholar
  3. Blakeman, R. H. 1990. The identification of crop disease and stress by aerial photography. In: Application of Remote Sensing in Agriculture, edited by M. D. Steven and J. A. Clark (Butterworths, London, UK), p. 229–254.Google Scholar
  4. Blazquez, C. H., Edwards, G. J. 1983Infrared color photography and spectral reflectance of tomato and potato diseasesJournal of Applied Photographic Engineering93337Google Scholar
  5. California Department of Pesticide Regulation (CDPR).2002Pesticide use in California indexed by chemicals and by commoditiesSacramentoCA, USAGoogle Scholar
  6. Chapelle, E. W., Kim, M. S. 1992Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of concentration of chlorophyll a, chlorophyll b and carotenoids in soybean leavesRemote Sensing of Environment18255267Google Scholar
  7. Colwell, R. N. 1956Determining the prevalence of certain cereal crop diseases by means of aerial photographyHilgardia26223286Google Scholar
  8. ENVI1999Environment for Visualization Images User’s GuideResearch System InstituteDenver, CO, USAGoogle Scholar
  9. Evens, K, Webster, R., Barker, A., Halford, P., Stafford, J., Griffin, S. 2003Mapping infestations of potato cyst nematodes and the potential for spatially varying application of nematicidesPrecision Agriculture4149162Google Scholar
  10. Fitzgerald, G. J., Maas, S. J., Detar, W. R. 2004Spider mite detection and canopy component mapping in cotton using hyperspectral imagery and spectral mixture analysisPrecision Agriculture5275289CrossRefGoogle Scholar
  11. Gitelson, A. A., Merzlyak, M. N. 1998Remote sensing of chlorophyll concentration in higher plant leavesAdvanced Space Research22689692Google Scholar
  12. Guyot, G. 1990. Optical properties of vegetation canopies. In: Applications of Remote Sensing in Agriculture, edited by M. D. Steven and J. Clark (Butterworths, London UK), p. 19–43.Google Scholar
  13. Hatfield, J. L., Pinter, P. J.,Jr. 1993Remote sensing for crop protectionCrop Protection12403414CrossRefGoogle Scholar
  14. Huete, A. C. 1988A soil adjusted vegetation index (SAVI)Remote Sensing of the Environment173753Google Scholar
  15. Huete, A. C., Justice, C., Liu, H. 1994Development of vegetation and soil indices for MODIS-EOSRemote Sensing of the Environment49224234CrossRefGoogle Scholar
  16. Keegan H. J., Schleter J. C., Hall W. A., Jr. and Haas G. M. 1956. Spectrophotometric and colorimetric study of diseased and rust resisting cereal crops Report (4591), National Bureau of Standards Washington, DC, USAGoogle Scholar
  17. Kimes, D. S., Markham, B. L., Tucker, C. J., Mcmurtrey, J. E. 1981Temporal relationships between spectral response and agronomic variables of a corn canopyRemote Sensing of Environment11401411CrossRefGoogle Scholar
  18. Kurschner, E., Walter, H., Koch, W. 1984Measurements of spectral reflectance of leaves as a method for assessing the infestation with powdery mildewJournal of Plant Disease Protection917180Google Scholar
  19. Lathrop, L. D., Pennypacker, S. 1980Spectral classification of tomato disease severity levelsPhotogrammetry Engineering and Remote Sensing4611331138Google Scholar
  20. Lillesand, T. M., Kiefer, R. W. 1994Remote Sensing and Image Interpretation3John Wiley & SonsNew York, USAGoogle Scholar
  21. Mcdonald, A. J., Gemmell, F. M., Lewis, P. E. 1998Investigation of the utility of spectral vegetation indices for determining information on coniferous forestsRemote Sensing of Environment66250272CrossRefGoogle Scholar
  22. Shibayama, M., Takahashi, W., Morinaga, S., Akiyama, T. 1993Canopy water deficit detection in paddy rice using a high resolution field spectrometerRemote Sensing of Environment45117126CrossRefGoogle Scholar
  23. Steven, M. D., Clark, J. A. 1990Applications of Remote Sensing in AgricultureButterworthsLondon, UK427Google Scholar
  24. Thomas, J. R., Oerther, G. F. 1972Estimating nitrogen content of sweet pepper leaves by reflectance measurementsAgronomy Journal641113CrossRefGoogle Scholar
  25. Toler, R. W., Smith, B. D., Harlan, J. C. 1981Use of aerial color infrared photography to evaluate crop diseasePlant Disease652431CrossRefGoogle Scholar
  26. Tucker, C. J. 1979Red and photographic infrared linear combinations for monitoring vegetationRemote Sensing of Environment8127150Google Scholar
  27. United States Department of Agriculture (USDA). (2002). The US processed tomato industry situation. National Agricultural Statistics Service. November (2003) accessedGoogle Scholar
  28. Wiegand, C. L., Richardson, A. J., Escobar, D. E., Gerbermann, A. H. 1991Vegetation indices in crop assessmentsRemote Sensing of Environment35105119CrossRefGoogle Scholar
  29. Yang, K., Lin, T. Y., Sun, J. B., Liu, J. 1988Digital Processing of Remotely Sensed ImagerySurveying & Mapping Press BeijingChinaGoogle Scholar
  30. Zhang, M., Ustin, S. L., Rejmankova, E., Sanderson, E. W. 1997Monitoring pacific coast marshes using remote sensingEcological Applications710391053Google Scholar
  31. Zhang, M., Liu, X., Oneill, M. 2002Spectral Discrimination of Phytophthora infestans infection on tomatoes based on principal component and cluster analysesInternational Journal of Remote Sensing2310951107CrossRefGoogle Scholar
  32. Zhang, M., Qin, Z., Liu, X., Ustin, S. L. 2003Hyperspectral remote sensing applications in detecting late blight infection on tomatoesInternational Journal of Applied Earth Observation and Geoinformation4295310CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  1. 1.Department of Land, Air and Water ResourcesUniversity of CaliforniaDavisUSA

Personalised recommendations