Hyperspectral remote sensing provides for significant advancement in the evaluation of the subtle changes in biophysical and biochemical attributes of the crop plants. Accurate estimates of leaf pigments, nitrogen, dry matter, water content, and leaf area index (LAI) from remotely sensed data can assist in determining the vegetation physiological state. In this paper, hyperspectral remote sensing measurements of the leaf reflectance were applied for assessing the effect of biotic stress (viral infection) on the spectral behaviour and biophysical variables of young potato plants, cultivar Agata, infected with Potato Virus Y (PVY). Spectral reflectance data were collected by means of a portable fiber-optics spectrometer in the visible and near infrared spectral ranges (350-1100 nm) with a spectral resolution of 1.5 nm. For the assessment of differences between the reflectance data of healthy and infected plant ata processing techniques, such as Student’s t-test, first derivative analyses, and estimation of vegetation indices, were applied. The analyses were performed in green, red, red edge and near infrared spectral ranges (450-850 nm) where the differences were the most significant and give information about changes in the chlorophyll and pigment content, moisture content, cells structures, and plant stress. Several vegetation indices (NDVI - Normalized Difference Vegetation Index, fD - Disease Index, SR –Simple Ratio, TCARI - Transformed Chlorophyll Absorption Reflectance Index, etc.) were computed and the best results for assessing the changes in the physiological state of the plants gave TCARI. A strong relationship was found between the results of the spectral analyses and the serological test DAS-ELISA was applied to assess the presence and the degree of the PVY infection.

1. J. R. Jensen, Remote sensing of the environment: an earth resource perspective, Upper Saddle River (NY), USA: Prentice-Hall, 2000.
2. S. Sankaran, A. Mishra, R. Ehsani and C. Davis, “A review of advanced techniques for detecting plant diseases,, Comput. Electron. Agric., vol. 72, pp. 1–13, 2010.
   DOI: 10.1016/j.compag.2010.02.007
3. K. Usha and B. Singh, “Potential applications of remote sensing in horticulture - A review,” Sci. Horticul., vol. 153, pp. 71–83, 2013.
   DOI: 10.1016/j.scienta.2013.01.008
4. A. A. Gitelson, “Wide dynamic range vegetation index for remote quantification of crop biophysical characteristics,” J. Plant Physiology, vol. 161, pp. 165–173, 2004.
   DOI: 10.1078/0176-1617-01176
   PMid: 15022830
5. S. Stagakis, N. Markos, O. Sykioti and A. Kyparissis, “Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: an application on a Phlomis fruticosa Mediterranean ecosystem using multangular CHRIS/PROBA observations,” Remote Sens. Environ., vol. 114, pp. 977–994, 2010.
   DOI: 10.1016/j.rse.2009.12.006
6. S. Delalieux, B. Somers, W. W. Verstraeten, J. A. N. Aardt, W. Keulemans and P. Coppin, “Hyperspectral indices to diagnose leaf biotic stress of apple plants considering leaf phenology,“ Int. J. Remote Sens., vol. 30, pp. 1887–1912, 2009.
   DOI: 10.1080/01431160802541556
7. D. Moshou, C. Bravo, J. West, S. Wahlen, A. McCartney and H. Ramon, “Automatic detection of yellow rust in wheat using reflectance measurements and neural networks,” Comput. Electron. Agric., vol. 44, pp. 173–188, 2004.
   DOI: 10.1016/j.compag.2004.04.003
8. M. Prabhakar, Y. G. Prasad, M. Thirupathi, G. Sreedevi, B. Dharajothi and B. Venkateswarlu, “Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae),” Comput. Electron. Agric., vol. 79, no. 2, pp. 189–198, Nov. 2011.
   DOI: 10.1016/j.compag.2011.09.012
9. V. K. Shettigara, D. O’Mara, T. Bubner and S. G. Kempinger, “Hyperspectral Band Selection Using Entropy and Target to Clutter Ratio Measures,” in Proc. of the 10th Australasian Remote Sensing and Photogrammetry Conference, Adelaide, S. Aust., Australia: Causal Productions, 2000, pp. 1008-1018.
10. W. Zhang and S. Sriharan, "Using hyperspectral remote sensing for land cover classification," in SPIE Proc. 5655 Multispectral and Hyperspectral Remote Sensing Instruments and Applications II, 2005, pp. 261-270.
    DOI: 10.1117/12.578104
11. J. L. Hatfield, A. A. Gitelson, J. S. Schepers and C. Walthall, “Application of spectral remote sensing for agronomic decisions,” Agron. J., vol. 100, pp. S117-S131, 2008.
    DOI: 10.2134/agronj2006.0370c
12. I. Filella, A. Porcar-Castell, S. Munne-Bosch, J. Back, M. Garbulsky and J. Penuelas, “PRI assessment of long-term changes in carotenoids/chlorophyll ratio and short-term changes in de-epoxidation state of the xanthophyll cycle,“ Int. J. Remote Sens., vol. 30, pp. 4443-4455, 2009.
    DOI: 10.1080/01431160802575661
13. J. B. Campbell, Introduction to Remote Sensing, London, UK: Taylor and Francis, 1996, p. 622.
14. G. R. Mahajan, R. N. Sahoo, R. N. Pandey, V. K. Gupta, and D. Kumar, “Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.),” Precision Agric, vol. 15, issue 2, pp. 227–240, 2014.
    DOI: 10.1007/s11119-014-9348-7
15. K. R. Manjunath et al., “Developing spectral library of major plant species of Western Himalayas using ground observations,” J. Indian Soc. Remote Sens., vol. 42, no. 1, pp. 201–216, 2014.
    DOI: 10.1007/s12524-013-0305-0
16. J. R. Jensen, Remote sensing of the environment: an earth resource perspective, Upper Saddle River (NY), USA: Prentice Hall, 2007.
17. R. Ranjan, U. K. Chopra, R. N. Sahoo, A. K. Singh and S. Pradhan, “Assessment of plant nitrogen stress through hyperspectral indices,” Int. J. Remote Sens., vol. 22, issue 20, pp. 6342–6360, 2012.
    DOI: 10.1080/01431161.2012.687473
18. Y. M. Govaerts, M. M. Verstraete, B. Pinty and N. Gobron, “Designing optimal spectral indices: A feasibility and proof of concept study,” Int. J. Remote Sens., vol. 20, pp. 1853–1873, 1999.
    DOI: 10.1080/014311699212524
19. I. Filella and J. Penuelas, “The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status,” Int. J. Remote Sens., vol. 15, no. 7, pp. 1459-1470, 1994.
    DOI: 10.1080/01431169408954177
20. J. G. Clevers, S. M. De Jong, G. F. Epema, F. Van der Meer, W. Bakker and A. K. Skidmore, “Derivation of the red edge index using MERIS standard band setting’’, Int. J. Remote Sens., vol. 23, no. 16, pp. 3169-3184, 2002.
    DOI: 10.1080/01431160110104647
21. H. Ross, ‘’Potato breeding: problems and perspectives,’’ J. Plant Breeding, Supplement 13: Advances in Plant Breeding, Berlin and Hamburg, Germany: Parey, pp. 66-75, 1986.
22. T. Valkonen, “Viruses: Economic losses and biotechnological potential,” in: Potato biology and biotechnology - advances and perspectives, eds. D. Vreugdenhil, E. J. Bradshow, Oxford, UK: San Diego (CA), USA: Elsevier, 2007, pp. 619-641.
23. J. A. de Bokx and H. Huttinga, “Potato virus Y. CMI/AAB”, in Descriptions of Plant Viruses, Commonwealth Agricultural Bureaux/Association of Applied Biologists, No. 242, 1981.
24. N. Petrov, “Potato virus Y (PVY) in crop species from the family Solanaceae,” Ph. D. dissertation, ISSAPP “N. Pushkarov”, Sofia, Bulgaria, 2012.
25. D. Noordam, Identification of plant viruses: Methods and experiments, Wageningen, The Netherlands: Centre for Agricultural Publishing and Documentation, 1973, p. 207.
26. M. Clark and A. Adams, “Characteristics of the microplate method of enzyme linked immunosorbent assay for the detection of plant viruses,” J. Gen. Virol., vol. 34, pp. 475-483, 1977
    DOI: 10.1099/0022-1317-34-3-475
    PMid: 323416
27. G. P. Asner, ”Biophysical and biochemical sources of variability in canopy reflectance,’’ Remote Sens. Environ., vol. 64, no. 3, pp. 234-253, Jun. 1998.
28. P. S. Thenkabail, R. B. Smith and E. Pauw, “Hyperspectral vegetation indices and their relationships with agricultural crop characteristics,” Remote Sens. Environ., vol. 71, pp. 158-182, 2000.
    DOI: 10.1016/S0034-4257(99)00067-X
29. S. L. Ustin, D. A. Roberts, R. O. Green, R. J. Zomer and M. Garcia, “Remote sensing methods monitor natural resources”, Photon. Spectra, vol. 33, no. 10, pp. 108–113, 1999.
30. B. Pinty, T. Lavergne, J. L. Widlowsky, N. Gobron and M. M. Verstraete, “On the need to observe vegetation canopies in the near-infrared to estimate visible light absorption”, Remote Sens. Environ., vol. 113, pp. 10–23, 2009.
    DOI: 10.1016/j.rse.2008.08.017
31. D. Krezhova, T. Yanev, St. Lukov, P. Pavlova, V. Aleksieva, D. Hristova and S. Ivanov, “Method for detecting stress induced changes in leaf spectral reflectance,” C. R. Acad. Bulg. Sci., vol. 58, no 5, pp. 517-522, 2005.
32. D. D. Krezhova, N. M. Petrov and S. N. Maneva, “Hyperspectral remote sensing applications for monitoring and stress detection in cultural plants: viral infections in tobacco plants,“ in Proc. of Remote Sensing for Agriculture, Ecosystems, and Hydrology Conf., Edinburgh, UK, 2012, vol. 8531, pp. 24-27.
    DOI: 10.1117/12.974722
33. P. S. Thenkabail, R. B. Smith and E. Pauw, ’’Hyperspectral vegetation indices and their relationships with agricultural crop characteristics,’’ Remote Sens. Environ., vol. 71, pp. 158–182, 2000.
    DOI: 10.1016/S0034-4257(99)00067-X
34. Y. Lee, C. Yang, K. Chang and Y. Shen, ‘’A simple spectral index using reflectance of 735 nm to assess nitrogen status of rice canopy,’’ Agron. J., vol. 100, pp. 202-212, 2008.
    DOI: 10.2134/agrojnl2007.0018
35. J. Hunt, E. Ramond and B. N. Rock, ‘’Detection in changes in leaf water content using near and mid-infrared reflectance,’’ Remote Sens. Environ., vol. 30, no 1, pp. 43–54, Oct. 1989.
    DOI: 10.1016/0034-4257(89)90046-1