Journal of the Indian Society of Remote Sensing

, Volume 44, Issue 6, pp 875–883 | Cite as

Detection and Classification of Mosaic Virus Disease in Cassava Plants by Proximal Sensing of Photochemical Reflectance Index

  • Sadasivan Nair Raji
  • Narayanan SubhashEmail author
  • Velumani Ravi
  • Raju Saravanan
  • Changatharayil N. Mohanan
  • Thangaraj MakeshKumar
  • Sukumar Nita
Short Note


Cassava Mosaic virus Disease (CMD) is the most severe and widespread virus infection that affects cassava (Manihot esculenta Crantz) crops. This paper investigates the application of photochemical reflectance index (PRI) imaging to detect and assess the impact of varying levels of CMD infection in cassava. Towards this, narrow band reflectance images of field-grown cassava plants were recorded at 531 and 571 nm by proximal sensing with a multispectral imaging system (MSIS). It was observed that the PRI values increase with increasing levels of CMD infection in all the varieties of cassava studied. A scatter plot of the PRI image intensity yielded a sensitivity of 85 % and specificity of 79 % for discriminating visibly no CMD from initial CMD and a sensitivity of 93 % and specificity of 92 % for discriminating initial CMD from advanced CMD. Area under the receiver operator characteristics (AUC-ROC) curve was used to discriminate the CMD infection level by differentiating visibly no CMD from initial CMD [AUC = 0.92] and initial CMD from advanced CMD [AUC = 0.99]. It was observed that PRI values determined from the experimental data follow a linear inverse relationship with net photosynthetic rate (Pn) (R 2 = 0.76) and total leaf chlorophyll (Chl) content (R 2 = 0.80). The results show that PRI imaging can be utilized to discriminate healthy plants from CMD and other stress infected crops by proximal sensing in outdoor plants.


Photochemical reflectance index Proximal sensing Cassava mosaic disease Virus infection AUC-ROC 



This work was carried out as part of a collaborative project between National Centre for Earth Sciences (NCESS), Thiruvananthapuram and Central Tuber Crop Research Institute (CTCRI), Thiruvananthapuram with grants from the NCESS Plan-289 project. The authors are thankful to the Directors of NCESS and CTCRI, the project assistant (Renju Appukuttan), and the technical staff involved in the work for their encouragement and support. RSN acknowledges NCESS for her research fellowship.


  1. Arnon, D. I. (1949). Copper enzymes in isolated chloroplasts and polyphenol oxidase in Beta vulgaris. Plant Physiology, 24, 1–15.CrossRefGoogle Scholar
  2. Ayanru, D. K. G., & Sharma, V. C. (1982). Effects of Cassava Mosaic Disease on Certain Leaf Parameters of Field-grown cassava clones. Phytopathology, 72, 1057–1059.CrossRefGoogle Scholar
  3. Barrs, H. D., & Weatherley, P. E. (1962). A re-examination of the relative turgidity technique for estimating water deficit in leaves. Australian Journal of Biological Sciences, 15, 413–428.CrossRefGoogle Scholar
  4. Bilger, W., & Bjorkman, O. (1990). Role of the xanthophyll cycle in photoprotection elucidated by measurements of light- induced absorbance changes, fluorescence and photosynthesis in leaves of Hedira canariencis. Photosynthesis Research, 25, 173–185.CrossRefGoogle Scholar
  5. Black, C. A. (1965). Methods of soil analysis: Part I Physical and mineralogical properties. In American Society of Agronomy. Madison, Wisconsin: USA.Google Scholar
  6. Carter, G. A. (1994). Ratios of leaf reflectance in narrow wavebands as indicators or plant stress. International Journal of Remote Sensing, 15, 697–703.CrossRefGoogle Scholar
  7. Carter, G. A., & Miller, R. L. (1994). Early Detection of Plant Stress by Digital Imaging within Narrow Stress-Sensitive Wavebands. Remote Sensing of Environment, 50, 295–302.CrossRefGoogle Scholar
  8. Deery, D., Jimenez-Berni, J., Jones, H., Sirault, X., & Furbank, R. (2014). Proximal remote sensing buggies and potential applications for field based phenotyping. Agronomy, 5, 349–379.CrossRefGoogle Scholar
  9. Demmig-Adams, B., & Adams, B. (1994). The role of xanthophyll cycle carotenoids in the protection of photosynthesis. Trends in Plant Science, 1, 21–26.CrossRefGoogle Scholar
  10. Esau, K. (1956). An anatomical view of virus disease. American Journal of Botany, 43, 739–748.CrossRefGoogle Scholar
  11. Fang, Z., Bouwkamp, J., & Solomos, T. (1998). Chlorophyllase activities and chlorophyll degradation during leaf senescence in non-yellowing mutant and wild type of Phaseolus vulgaris L. Journal of Experimental Botany, 49, 503–510.Google Scholar
  12. Filella, I., Porcar-Castell, A., Munne-Bosch, S., Back, J., Garbulsky, M. F., & Penuelas, J. (2009). PRI assessment of long-term changes in carotenoids/chlorophyll ratio and short-term changes in de-epoxidation state of the xanthophyll cycle. International Journal of Remote Sensing, 30, 4443–4455.CrossRefGoogle Scholar
  13. Gamon, J. A., Peñuelas, J., & Field, C. B. (1992). A narrow-wave band spectral index that track diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41, 35–44.CrossRefGoogle Scholar
  14. Gamon, J. A., Serrano, L., & Surfus, J. S. (1997). The Photochemical Reflectance Index: An optical indicator of photosynthetic radiation use efficiency across species, functional types and nutrient levels. Oecologia, 112, 492–501.CrossRefGoogle Scholar
  15. Ibaraki, Y., Matsumura, K., & Gupta, S. D. (2010). Low Cost Photochemical reflectance index measurements of micro propagated plantlets using image analysis. Computers and Electronics in Agriculture, 71, 170–175.CrossRefGoogle Scholar
  16. Mandal, M., Saravanan, R., & Maiti, S. (2008). Effect of different levels of N, P and K on downy mildew (Peronospora plantaginis) and seed yield of isabgol (Plantago ovate). Crop Protection, 27, 988–955.CrossRefGoogle Scholar
  17. Nakaji, T., Oguma, H., & Fujinuma, Y. (2006). Seasonal changes in the relationship between photochemical reflectance index and photosynthetic light use efficiency of Japanese larch needles. International Journal of Remote Sensing, 27, 493–509.CrossRefGoogle Scholar
  18. Nichol, C. J., & Grace, J. (2010). Determination of leaf pigment content in Calluna vulgaris shoots from spectral reflectance. International Journal of Remote Sensing, 31, 5409–5422.CrossRefGoogle Scholar
  19. Palta, J. (1990). Leaf chlorophyll content. Instrumentation for studying vegetation canopies for Remote Sensing in Optical and Thermal Infrared Regions. Remote Sensing Reviews, 5(1), 207–213.Google Scholar
  20. Panigada, C., Rossini, M., Meroni, M., Cilia, C., Busetto, L., Amaducci, S., Boschetti, M., Cogliati, S., Picchi, V., Pinto, F., Marchesi, A., & Colombo, R. (2014). Fluorescence, PRI and canopy temperature for water stress detection in cereal crops. International Journal of Applied Earth Observation and Geoinformation, 30, 167–178.Google Scholar
  21. Patil, B. L., & Fauquet, C. M. (2009). Cassava mosaic geminiviruses: actual knowledge and perspectives. Molecular Plant Pathology., 10(5), 685–701.CrossRefGoogle Scholar
  22. 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, 135–146.CrossRefGoogle Scholar
  23. Penuelas, J., Filella, I., & Gamon, J. A. (1995). Assessment of photosynthetic radiation use efficiency with spectral reflectance. New Phytologist, 131, 291–296.CrossRefGoogle Scholar
  24. Penuelas, J., Llusia, J., Pinol, J., & Filella, I. (1997). Photochemical reflectance index and leaf photosynthetic radiation use efficiency assessment in Mediterranean trees. International Journal of Remote Sensing, 18, 2863–2868.CrossRefGoogle Scholar
  25. Raji, S. N., Ravi, V., Saravanan, R., Subhash, N., Makeshkumar, T., Nita, S. & Renju, U. A. (2013), Assessing cassava mosaic virus infection in cassava plants using PRI imaging. Proceedings of National Symposium on Pathogenomics for Diagnosis and Management of Plant Diseases, Thiruvananthapuram, India, October 25–26.Google Scholar
  26. Raji, S. N., Subhash, N., Ravi, V., Saravanan, R., Mohanan, C. N., Nita, S., & Makeshkumar, T. (2015). Detection of mosaic virus disease in cassava plants by sunlight-induced fluorescence imaging: a pilot study for proximal sensing. International Journal of Remote Sensing, 36(11), 2880–2897.CrossRefGoogle Scholar
  27. Richardson, A. D., & Berlyn, G. P. (2002). Spectral reflectance and photosynthetic properties of Betula papyrifera (Betulaceae) leaves along an elevational gradient on Mt. Mansfield, Vermont, USA. American Journal of Botany, 89, 88–94.CrossRefGoogle Scholar
  28. Richardson, A. D., Berlyn, G. P., & Duigan, S. P. (2003). Reflectance of Alaskan black spruce foliage in relation to elevation and latitude. Tree Physiology, 23, 537–544.CrossRefGoogle Scholar
  29. Subhash, N., Mohanan, C. N., Rupananda, J. M., & Muralidharan, V. (2004). Quantification of stress adaptation by laser-induced fluorescence spectroscopy of plants exposed to engine exhaust emission and drought. Functional Plant Biology, 31, 709–719.CrossRefGoogle Scholar
  30. Terry, E.R. (1975). Description and evaluation of cassava mosaic disease in Africa. Proceedings of Interdisciplinary workshop held at IITA, Ibadan, Nigeria, 75, 53–54.Google Scholar
  31. Thresh, J. M., Fargette, D., & Otim Nape, G. W. (1994). Effects of African cassava mosaic geminivirus on the yield of cassava. Tropical Science, 34, 26–42.Google Scholar
  32. Tu, J. C., Ford, R. E., & Krass, C. J. (1968). Comparison of chloroplasts and photosynthetic rates of plants infected and not infected by maize dwarf mosaic virus. Phytopathology, 58, 285–288.Google Scholar
  33. Whitehead, D., Boelman, N. T., Turnbull, M. H., Griffin, K. L., Tissue, D. T., Barbour, M. M., Hunt, J. E., Richardson, S. J., & Peltzer, D. A. (2005). Photosynthesis and reflectance indices for rainforest species in ecosystems undergoing progression along a soil fertility chronosequence in New Zealand. Oecologia, 144, 233–244.Google Scholar

Copyright information

© Indian Society of Remote Sensing 2016

Authors and Affiliations

  • Sadasivan Nair Raji
    • 1
  • Narayanan Subhash
    • 1
    • 3
    Email author
  • Velumani Ravi
    • 2
  • Raju Saravanan
    • 2
  • Changatharayil N. Mohanan
    • 1
  • Thangaraj MakeshKumar
    • 2
  • Sukumar Nita
    • 1
  1. 1.National Center for Earth Science StudiesThiruvananthapuramIndia
  2. 2.Central Tuber Crops Research InstituteThiruvananthapuramIndia
  3. 3.Forus Health Pvt Ltd.BangaloreIndia

Personalised recommendations