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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2026, V. 23, No. 4, pp. 258-271

Study of reflective properties of potato plants affected by late blight using ground-based hyperspectral measurements

A.M. Shpanev 1 , D.V. Rusakov 1 , V.V. Smuk 2 , A.V. Khyutti 2 
1 Agrophysical Research Institute, Saint Petersburg, Россия
2 All-Russian Institute of Plant Protection, Pushkin, Saint Petersburg, Russia
Accepted: 16.06.2026
DOI: 10.21046/2070-7401-2026-23-4-258-271
Late blight is one of the most widespread and particularly dangerous potato diseases in northwestern Russia. This circumstance determines the importance of timely detection of plant infestation and fungicide treatments even before visible symptoms of leaf damage appear. Current instrumentation capabilities suggest that this problem can be solved using hyperspectral imaging. A study of the reflectivity characteristics of potato plants affected and unaffected by late blight was conducted at the experimental base of the Menkovskiy branch of the Agrophysical Research Institute in 2020 and 2022. The objects of hyperspectral ground-based measurements were plants of susceptible (Lanorma) and resistant (Sineglazka) potato varieties, fertilized (N90P90K90) and unfertilized (N0P0K0), unaffected (4-fold fungicide treatment) and heavily affected by late blight (artificial infection background). The study revealed that as the disease progressed, plant reflectance decreased, particularly in susceptible varieties and those treated with complete mineral fertilizer, which were more severely affected by late blight. A significant decrease in the spectral radiance coefficient was observed in the green spectrum on the 5th day after artificial infection, and in the near infrared range on the 20th and 29th days. Using spectral indices reflecting pigment content in leaves allowed us to identify changes in the reflectance of potato plants affected by late blight. The most promising were the carotenoid reflectance indices CAR1 (Carotenoid Reflectance Index 1) and CAR2 (Carotenoid Reflectance Index 2) whose values significantly increased under the influence of the pathogen in the vast majority of measurements, including the earliest, conducted on the second day after infection. The Gitelson and Merzlyak indices GM1 (Gitelson and Merzlyak Index 1) and GM2 (Gitelson and Merzlyak Index 2) responded best to the infection of potato plants with late blight on the 12th day, and the flavonol index FRI (Reflection Index of Flavonols), due to the small number of cases with reliable differences in the reflective properties of plants with different degrees of damage by late blight, turned out to be less reliable and suitable for solving our problem.
Keywords: potato (Solanum tuberosum L.), late blight, artificial infectious background, varietal resistance, mineral fertilizers, ground-based hyperspectral measurements, spectral radiance coefficient, spectral indices
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References:

  1. Bekmukhamedov N. E., Karabkina N. N., Changes in the spectral characteristics of spring wheat plants infected with fungal diseases, Sel’skoe, lesnoe i vodnoe khozyaistvo, 2013, No. 10 (in Russian), http://agro.snauka.ru/2013/10/1169.
  2. Belov D. A., Khyutti A. V., Potato late blight and its control program, Kartofel’ i ovoshchi, 2018, No. 8, pp. 19–21 (in Russian).
  3. Vasfilova Ye. S., The importance of biologically active compounds for increasing plant resistance to adverse abiotic influences, Rastitel’nye resursy, 2024, V. 60, No. 3, pp. 3–20 (in Russian), DOI: 10.31857/S0033994624030013.
  4. Gurova T. A., Klimenko D. N., Lugovskaya O. S. et al., Spectral characteristics of wheat varieties under biotic stress, Dostizheniya nauki i tekhniki APK, 2019, V. 33, No. 10, pp. 71–75 (in Russian), DOI: 10.24411/0235-2451-2019-11016.
  5. Danilov R. Yu., Ismailov V. Ya., Tret’yakov V. A. et al., Development of precision technologies for phytosanitary monitoring of agroecosystems based on the use of remote hyperspectral sensing data of the Earth, Dostizheniya nauki i tekhniki APK, 2018, V. 32, No. 10, pp. 82–86 (in Russian), DOI: 10.24411/0235-2451-2018-11019.
  6. Danilov R. Yu., Kremneva O. Yu., Ismailov V. Ya. et al., General methods and results of ground hyperspectral studies of seasonal changes in the reflective properties of crops and certain types of weeds, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, V. 17, No. 1, pp. 113–127 (in Russian), DOI: 10.21046/2070-7401-2020-17-1-113-127.
  7. Danilov R. Yu., Kremneva O. Yu., Pachkin A. A., Study of spectral characteristics of winter barley plants infected with pathogens of economically significant diseases, Trudy Kubanskogo gosudarstvennogo agrarnogo universiteta, 2021, No. 93, pp. 111–121 (in Russian), DOI: 10.21515/1999-1703-93-111-121.
  8. Derevyagina M. K., Vasil’yeva S. V., Zeyruk V. N., Belov G. L., Colloidal silver-containing preparations against rhizoctonia, alternaria, and late blight of potatoes, Izvestiya Timiryazevskoi sel’skokhozyaistvennoi akademii, 2020, No. 4, pp. 54–66 (in Russian), DOI: 10.26897/0021-342X-2020-4-54-66.
  9. Ismailov E. Ya., Nadykta V. D., Ismailov V. Ya., Kosten’ko I. A., Shvets A. A., Hyperspectral studies of damage to agricultural crops by phytopathogens, Kosmonavtika i raketostroenie, 2012, No. 3 (68), pp. 98–103 (in Russian).
  10. Kondrat’yev K. Ya., Fedchenko P. P., Spektral’naya otrazhatel’naya sposobnost’ i raspoznavanie rastitel’nosti (Spectral reflectivity and vegetation recognition), Leningrad: Gidrometeoizdat, 1982, 216 p. (in Russian).
  11. Kremneva O. Yu., Tutubalina O. V., Sereda I. I. et al., Studies of changes in the spectral characteristics of winter wheat varieties depending on the degree of infection with pathogens, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, V. 17, No. 3, pp. 149–161 (in Russian), DOI: 10.21046/2070-7401-2020-17-3-149-161.
  12. Kremneva O. Yu., Danilov R. Yu., Tutubalina O. V., Sereda I. I., Changes in the spectral characteristics of winter wheat depending on the degree of development of yellow leaf spot, Materialy 9-i Mezhdunarodnoi nauchnoi konferentsii “Regional’nye problemy distantsionnogo zondirovaniya Zemli” (Proc. 9th Intern. Scientific Conf. “Regional Problems of Remote Sensing of the Earth”), Krasnoyarsk: SFU, 2022, pp. 242–246 (in Russian).
  13. Metodicheskie ukazaniya po registratsionnym ispytaniyam fungitsidov v sel’skom khozyaistve (Guidelines for registration tests of fungicides in agriculture), Saint-Petersburg: Vserossiiskii nauchno-issledovatel’skii institut zashchity rastenii, 2009, 378 . (in Russian).
  14. Mudarisov S. G., Miftakhov I. R., Farkhutdinov I. M., Use of unmanned technologies and machine learning methods for detection of sugar beet diseases, Agropromyshlennyi kompleks Rossii, 2025, V. 32, No. 5, pp. 674–684 (in Russian), DOI: 10.55934/2587-8824-2025-32-4-674-684.
  15. Sokolov Yu. G., Sadkovskiy V. T., Kremneva O. Yu. et al., Development of technology for detecting foci of rust diseases of wheat, Mezhdunarodnyi nauchno-issledovatel’skii zhurnal, 2018, No. 12-2 (78), pp. 29–33 (in Russian), DOI: 10.23670/IRJ.2018.78.12.042.
  16. Chugunov V. S., Shatilova O. N., Uskova L. B., Anisimov B. V., Potato imports in Russia in 2014–2015, Kartofel’ i ovoshchi, 2016, No. 5, pp. 33–35 (in Russian).
  17. Shpanev A. M., Experimental basis for remote sensing of phytosanitary condition of agroecosystems in the North-West of the Russian Federation, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, V. 16, No. 3, pp. 61–68 (in Russian), DOI: 10.21046/2070-7401-2019-16-3-61-68.
  18. Shpanev A. M., Fesenko M. A., Smuk V. V., Efficiency of using mineral fertilizers and an integrated plant protection system in field crop rotation in the North-West of the Russian Federation, Agrokhimiya, 2021, No. 1, pp. 12–22 (in Russian), DOI: 10.31857/S0002188121010099.
  19. Barnes J. D., Balaguer L., Manrique E. et al., A reappraisal of the use of DMSO for the extraction and determination of chlorophyll a and b in lichens and higher plants, Environmental and Experimental Botany, 1992, V. 32 (2), pp. 85–100, DOI: 10.1016/0098-8472(92)90034-Y.
  20. Gamon J. A., Peñuelas J., Field C. B., A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency, Remote Sensing of Environment, 1992, V. 41 (1), pp. 35–44, DOI: 10.1016/0034-4257(92)90059-S.
  21. Gitelson A. A., Merzlyak M. N., Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing, J. Plant Physiology, 1996, V. 148(3–4), pp. 494–500, DOI: 10.1016/S0176-1617(96)80284-7.
  22. Gitelson A. A., Merzlyak M. N., Chivkunova O. B., Optical properties and nondestructive estimation of anthocyanin content in plant leave, Photochemistry and Photobiology, 2001, V. 74 (1), pp. 38–45, DOI: 10.1562/0031-8655(2001)074<0038:OPANEO>2.0.CO;2.
  23. Gitelson A. A., Zur Y., Chivkunova O. B., Merzlyak M. N., Assessing carotenoid content in plant leaves with reflectance spectroscopy, Photochemistry and Photobiology, 2002, V. 75(3), pp. 272–281, DOI: 10.1562/0031-8655(2002)0750272ACCIPL2.0.CO2.
  24. Merzlyak M. N., Gitelson A. A., Chivkunova O. B., Rakitin V. Y., Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening, Physiologia Plantarum, 1999, V. 106, pp. 135–141, DOI: 10.1034/j.1399-3054.1999.106119.x.
  25. Merzlyak M. N., Solovchenko A. E., Smagin A. I., Gitelson A. A., Apple flavonols during fruit adaptation to solar radiation: spectral features and techniques for non-destructive assessment, J. Plant Physiology, 2005, V. 162 (2), pp. 151–160, DOI: 10.1016/j.jplph.2004.07.002.
  26. Peñuelas J., Gamon J., Freeden A. et al., Reflectance indices associated with physiological changes in nitrogen and water limited sunflower leaves, Remote Sensing of Environment, 1994, V. 48 (2), pp. 135–146, DOI: 10.1016/0034-4257(94)90136-8.
  27. Peñuelas J., Baret F., Filella I. (1995a), Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance, Photosynthetica, 1995, V. 31 (2), pp. 221–230.
  28. Peñuelas J., Filella I., Lloret P. et al. (1995b), Reflectance assessment of mite effects on apple trees, Intern. J. Remote Sensing, 1995, V. 16 (14), pp. 2727–2733, DOI: 10.1080/01431169508954588.
  29. Sims D. A., Gamon J. A., Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages, Remote Sensing of Environment, 2002, V. 81 (2–3), pp. 337–354, DOI: 10.1016/S0034-4257(02)00010-X.