Abstract
A promising direction in the field of plant protection is the creation of remote sensing methods to detect the development of plant diseases based on the analysis of the reflected radiation spectra. Spectral analysis is a relatively simple non-invasive method that allows to obtain high quality data. The objects of the study were 6 varieties of winter wheat with different levels of resistance to tan spot (Pyrenophora tritici-repentis). The purpose of the present study was to assess, in the field conditions, the informativeness of the spectral optical parameters of winter wheat varieties and their mixtures with different susceptibility to the tan spot pathogen. Using the ASD FieldSpec 3 Hi-Res spectroradiometer, high-precision ground-based measurements of selected plant objects were carried out. Analysis of changes in the morphology of spectral signatures of the studied objects showed that the most promising spectral ranges for detecting the development of tan spot at the early stages are 1445–1775 nm and 2050–2450 nm. According to the results of univariate analysis of variance, the influence of wheat varieties and the degree of disease development was confirmed. The hypothesis that the less resistant the wheat variety to tan spot is, the faster is the decrease in the spectral brightness coefficient (SBC) values in the near infrared range of the spectrum and the increase in the red range of the spectrum was confirmed.
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Abdullah, S., Sehgal, S. K., Ali, S., Liatukas, Z., Ittu, M., & Kaur, N. (2017). Characterization of Pyrenophora tritici-repentis (Tan spot of wheat) races in Baltic states and Romania. Plant Pathology Journal, 33(2), 133–139. https://doi.org/10.5423/PPJ.OA.10.2016.0214.
Ali, S., Gurung, S., & Adhikari, T. B. (2010). Identification and characterization of novel isolates of Pyrenophora tritici-repentis from Arkansas. Plant Disease, 94, 229–235. https://doi.org/10.1094/pdis-94-2-0229.
Ashourloo, D., Mobasheri, M. R., & Huete, A. (2014). Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements. Remote Sensing, 6, 5107–5123. https://doi.org/10.3390/rs6065107.
Azadbakhta, M., Ashourlooa, D., Aghighia, H., Radiomb, S., & Alimohammadic, A. (2019). Wheat leaf rust detection at canopy scale under different LAI levels usingmachine learning techniques. Computers and Electronics in Agriculture, 156, 119–128. https://doi.org/10.1016/j.compag.2018.11.016.
Barot, S., Allard, V., Cantarel, A., Enjalbert, J., Gauffreteau, A., Goldringer, I., et al. (2017). Designing mixtures of cultivars for multifunctional agriculture with the help of ecology. A review. Agronomy for Sustainable Development, 37, 13. https://doi.org/10.1007/s13593-017-0418-x.
Benito, M. T. J., Ojeda, C. B., & Rojas, F. S. (2008). Process analytical chemistry: Applications of near infrared spectrometry in environmental and food analysis: An overview. Applied Spectroscopy Reviews, 43(5), 452–484. https://doi.org/10.1080/05704920802031382
Bhandaria, M., Ibrahima, A. M. H., Xueb, Q., Jungd, J., Changd, A., Ruddb, J. C., Maedac, M., Rajana, N., Neelya, H., & Landivar, J. (2020). Assessing winter wheat foliage disease severity using aerial imagery acquired from small Unmanned Aerial Vehicle (UAV). Computers and Electronics in Agriculture, 176, 105665. https://doi.org/10.1016/j.compag.2020.105665
Calderón, R., Navas-Cortés, J. A., Lucena, C., & Zarco-Tejada, P. J. (2013). High-resolution airborne hyperspectral and thermal imagery for early detection of Verticilliumwilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing of Environment, 139, 231–245. https://doi.org/10.1016/j.rse.2013.07.031.
Danilov, R. Y., Kremneva, O. Y., Ismailov, V. Y., Krivoshein, V. V., & Pachkin, A. A. (2020). 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, 17(1), 113–127.
Dubs, F., Le Roux, X., Allard, V., Andrieu, B., Barot, S., Cantarel, A., et al. (2018). An experimental design to test the effect of wheat variety mixtures on biodiversity and ecosystem services. https://hal.archives-ouvertes.fr/hal-01843564
Eyal, Z., & Blum, A. (1989). Canopy temperature as a correlative measure for assessing host response to Septoria tritici blotch of wheat. Plant Disease, 73, 468–471. doi: https://doi.org/10.1094/pd-73-0468.
Eyal, Z., Scharen, A. L., Prescott, J. M., & Van Ginkel, M. (1987). The Septoria diseases of wheat: concepts and methods of disease management. CIMMYT.
Fernandez, M. R., DePauw, R. M., Clarke, J. M., & Fox, S. L. (1998). Discoloration of wheat kernels by Pyrenophora tritici-repentis. Canadian Journal of Plant Pathology, 20, 380–383. https://doi.org/10.1080/07060669809500407.
Fieldspec 3 User Manual. (2010). ASD Document 600540 Rev. J ASD Inc.,110pp. http://www.geo-informatie.nl/courses/grs60312/material2017/manuals/600540-jfieldspec3usermanual.pdf
Huang, W. J., David, W. L., Niu, Z., Zhang, Y. J., Liu, L. Y., & Wang, J. H. (2007). Identification of yellow rust in wheat using in situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture, 8(5), 187–197. https://doi.org/10.1007/s11119-007-9038-9.
Jackson, D. (1982). Canopy temperature and crop water stress. Advances in Irrigation. https://doi.org/10.1016/B978-0-12-024301-3.50009-5
Kamel, S., Cherif, M., Hafez, M., Despins, T., & Aboukhaddour, R. (2019). Pyrenophora tritici–repentis in Tunisia: Race structure and effector genes. Frontiers in Plant Science, 10, 1562. https://doi.org/10.3389/fpls.01562
Kang, Y., Anderegg, J., Mikaberidze, A., Karisto, P., Masche, F., McDonald, B. A., Walter, A., & Hund, A. (2018). Hyperspectral canopy sensing of Wheat Septoria Tritici Blotch Disease. Frontiers in Plant Science, 9, 1195. https://doi.org/10.3389/fpls.2018.01195
Kokhmetova, A., Kremneva, J., Volkova, G., Atishova, M., & Sapakhova, Z. (2017). Evaluation of wheat cultivars growing in Kazakhstan and Russia for resistance to tan spot. Journal of Plant Pathology, 99(1), 161–167. https://doi.org/10.4454/jpp.v99i1.3812.
Kremneva, O. Y., & Volkova, G. V. (2007). Population structure of Pyrenophora tritici-repentis in the North Caucasus in terms of virulence and morphological cultural characteristics. Mikologiya I Fitopatologiya, 4, 356–361.
Kremneva, O. Y., Asaturova, A. M., Zharnikova, M. D., & Volkova, G. V. (2015). Strains of bacteria antagonists of Ppyrenophora tritici repentis in vitro, effective against wheat tan spot in the germination phase in the growing experiment. Sel’skokhozyaistvennaya Biologiya, 50(1), 99–106. https://doi.org/10.15389/agrobiology.2015.1.99eng.
Kremneva, O. Y., Volkova, G. V., & Kovalenko, N. (2019). The dynamics of the race structure of Рyrenophora tritici-repentisin the North Сaucasus region. Mikologiya I Fitopatologiya, 53(4), 246–253. https://doi.org/10.1134/S0026364819040056.
Kremneva, O. Y., Mironenko, N. V., Volkova, G. V., Baranova, O. A., Kim, Y. S., & Kovalenko, N. M. (2021). Resistance of winter wheat varieties to tan spot in the North Caucasus region of Russia. Saudi Journal of Biological Sciences, 28(3), 1787–1794. https://doi.org/10.1016/j.sjbs.2020.12.021.
Lee, W. S., Alchanatis, V., Yang, C., Hirafuji, M., Moshou, D., & Li, C. (2010). Sensing technologies for precision specialty crop production. Computers and Electronics in Agriculture, 74(1), 2–33. https://doi.org/10.1016/j.compag.2010.08.005
Lamari, L., & Bernier, C. C. (1989). Evaluation of wheat lines and cultivars to tan spot [Pyrenophora tritici-repentis] based on lesion type. Canadian Journal of Plant Pathology, 11(1), 49–56. https://doi.org/10.1080/07060668909501146.
Lamari, L., Strelkov, S. E., Yahyaoui, A., Orabi, J., & Smith, R. B. (2003). The identification of two new races of Pyrenophora tritici-repentis from the host centre of diversity confirms a one to one relationship in tan spot of wheat. Phytopathology, 93, 391–396. https://doi.org/10.5423/PPJ.OA.10.2016.0214.
Lowe, A., Harrison, N., & French, A. P. (2017). Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods, 13, 2–12. https://doi.org/10.1186/s13007-017-0233-z.
Mahlein, A. K., Rumpf, T., Welke, P., Dehne, H. W., Plümer, L., Steiner, U., et al. (2013). Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment, 128, 21–30. https://doi.org/10.1016/j.rse.2012.09.019.
Mahlein, A. K. (2016). Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease, 100, 241–251. https://doi.org/10.1094/PDIS-03-15-0340-FE.
Mikhailova, L. A., Mironenko, N. V., & Kovalenko, N. M. (2014). Populations of Pyrenophora tritici-repentis in the North Caucasus and North-West Russia: Racial composition and dynamics of virulence. Mikologiya I Fitopatologiya, 48(6), 393–400.
Mironenko, N. V., Baranova, O. A., Kovalenko, N. M., Mikhailova, L. A., & Rosseva., L. P. (2016). Genetic structure of the russian populations of Pyrenophora tritici-repentis, determined by using microsatellite markers. Russian Journal of Genetics, 52(8), 771–779. https://doi.org/10.1134/S1022795416080093.
Mishra, P., Asaari, M. S. M., Herrero-Langreo, A., Lohumi, S., Diezma, B., & Scheunders, P. (2017). Close range hyperspectral imaging of plants: a review. Biosystems engineering, 164, 4–9. https://doi.org/10.1016/j.biosystemseng.2017.09.009.
Momeni, H., Aboukhaddour, R., Javan-Nikkhah, M., Razavi, M., Naghavi, M. R., Akhavan, A., & Strelkov, S. E. (2014). Race identification of Pyrenophora tritici-repentis in Iran. Journal of Plant Pathology, 96(2), 287–294. https://doi.org/10.4454/JPP.V96I2.036.
Moshou, D., Bravo, C., Oberti, R., West, J. S., Ramon, H., Vougioukas, S., et al. (2011). Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops. Biosystems Engineering, 108(4), 311–321. https://doi.org/10.1016/j.biosystemseng.2011.01.003.
Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, A., McCartney, A., et al. (2005). Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real-Time Imaging, 11(2), 75–83. https://doi.org/10.1016/j.rti.2005.03.003.
Odilbekov, F., Armoniené, R., Henriksson, T., & Chawade, A. (2018). Proximal phenotyping and machine learning methods to identify Septoria tritici blotch disease symptoms in wheat. Frontiers in Plant Science, 9, 685. https://doi.org/10.3389/fpls.2018.00685.
Oliver, R., Lichtenzveig, J., Tan, K., Waters, C. O., Rybak, K., Lawrence, J., Friesen, T., & Burgess, P. (2014). Absence of detectable yield penalty associated with insensitivity to Pleosporales necrotrophic effectors in wheat grown in the West. Australian wheat belt. Plant Pathology, 63, 1027–1032. https://doi.org/10.1111/ppa.12191.
Phillips, L. B., Hansen, A. J., & Flather, C. H. (2008). Evaluating the species energy relationship with the newest measure of ecosystem energy: NDVI versus MODIS primary production. Remote Sensing of Environment, 112, 4381–4392. https://doi.org/10.1016/j.rse.2008.04.012.
Pitham, P. A., Ducati, J. R., Garrido, L. R., Arruda, D. C., Thum, A. B., & Hoff, R. (2021). Spectral characterization of fungal diseases downy mildew, powdery mildew, black-foot and Petri disease on Vitis vinifera leaves. Remote Sensing, 42, 5680–5697. https://doi.org/10.1080/01431161.2021.1929542
Sankaran, S., Mishra, A., Ehsani, R., & Davis, C. (2010). A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture, 72, 1–13. https://doi.org/10.1016/j.compag.2010.02.007.
Shabeer, A., & Bockus, W. W. (1988). Tan spot effects on yield and yield components relative to growth stage in winter wheat. Plant Disease, 72, 599–602. https://doi.org/10.1094/PD-72-0599.
Singh, P. K., Singh, R. P., Duveiller, E., Mergoum, M., Adhikari, T. B., & Elias, E. M. (2010). Genetics of wheat – Pyrenophora tritici-repentis interactions. Euphytica, 171, 1–13. https://doi.org/10.1007/s10681-009-0074-6.
Strelkov, S. E., & Lamari, L. (2003). Host-parasite interactions in tan spot [Pyrenophora tritici-repentis] of wheat. Canadian Journal of Plant Pathology, 25(4), 339–349. https://doi.org/10.1080/07060660309507089
Thenkabail, P. S., Lyon, J. G., & Huete, A. (2011). Hyperspectral remote sensing of vegetation. CRC Press. https://doi.org/10.1201/b11222
Volkova, G. V., Kremneva, O. Y., Kudinova, O. A., Vaganova, O. F., Matveeva, I. P., & Kim, Y. C. (2019). Phytosanitary assessment of winter wheat varieties sown in the south of Russia for a complex of diseases. Tavrichesky Bulletin of Agrarian Science, 3(19), 39–48. https://doi.org/10.33952/2542-0720-2019-3-19-39-48
Wakie, T. T., Kumar, S., Senay, G. B., Takele, A., & Lencho, A. (2016). Spatial prediction of wheat septoria leaf blotch (Septoria tritici) disease severity in Central Ethiopia. Ecological Informatics, 36, 15–30. https://doi.org/10.1016/j.ecoinf.2016.09.003.
Yang, K., Xue, Z., Li, H., Jia, T., & Cui, Y. (2013). New methodology of hyperspectral information extraction and accuracy assessment based on a neural network. Mathematical and Computer Modelling, 58, 644–660. https://doi.org/10.1016/j.mcm.2011.10.037.
Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E., & Tucker, C. J. (2014). The use of the Normalized Difference Vegetation Index (NDVI) to assess land degradation at multiple scales. Springer. https://doi.org/10.1007/978-3-319-24112-8
Yu, K., Anderegg, J., Mikaberidze, A., Karisto, P., Mascher, F., McDonald, B. A., Walter, A., & Hund, A. (2018). Hyperspectral canopy sensing of wheat Septoria tritici blotch disease. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2018.01195.
Yu, K., Leufen, G., Hunsche, M., Noga, G., Chen, X., & Bareth, G. (2014). Investigation of leaf diseases and estimation of chlorophyll concentration in seven barley varieties using fluorescence and hyperspectral indices. Remote Sensing, 6, 64–86. https://doi.org/10.3390/rs6010064.
Yuan, L., Pu, R., Zhang, J., Wang, J., & Yang, H. (2016). Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale. Precision Agriculture, 17, 332–348. https://doi.org/10.1007/s11119-015-9421-x.
Zadoks, J. C., Chang, T. T., & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research, 14, 415–421. https://doi.org/10.1111/j.1365-3180.1974.tb01084.x.
Zarco-Tejada, P. J., Miller, J. R., Noland, T. L., Mohammed, G. H., & Sampson, P. H. (2001). Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopie with hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 39, 1491–1507. https://doi.org/10.1109/36.934080
Zhang, J., Yuan, L., d Pu, R., d Loraamm, R. W., Yang, G., & Wanga, J. (2014). Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat. Computers and Electronics in Agriculture, 100, 79–87. https://doi.org/10.1016/j.compag.2013.11.001
Funding
Studies of the degree of development of the disease were carried out within the framework of the state task of the Ministry of Education and Science of Russia on the topic No. FGRN-2022-0001. The work on ground-based spectroradiometry of winter wheat varieties was supported by a grant from the Russian Foundation for Basic Research, the Administration of the Krasnodar Territory No. 19-416-230043 r_a.
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Kremneva, O.Y., Danilov, R.Y., Sereda, I.I. et al. Spectral characteristics of winter wheat varieties depending on the development degree of Pyrenophora tritici-repentis. Precision Agric 24, 830–852 (2023). https://doi.org/10.1007/s11119-022-09976-2
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DOI: https://doi.org/10.1007/s11119-022-09976-2