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Anpilogova, L. K. and Volkova, G. V. (2000). Methods of creating artificial infectious backgrounds and evaluating wheat varieties for resistance to harmful diseases (fusarium head blight, rust, powdery mildew), VNIIBZR, RASKHN, Krasnodar. pp: 28.
Avinash, P., Ramathilaga, A. and Valarmathi, P. (2022). Hyperspectral remote sensing for discrimination for plant disease forecasting: Review. J. Pharmacogn. Phytochem. 11: 208–15.
Chengzhi, C., Jidong, C. and Wenfang, C. (2024). Potential yields of two staple cereal crops worldwide under global warming. Farm. Manage. 9: 1-11.
Cheshkova, A. F. (2022). A review of hyperspectral image analysis techniques for plant disease detection and identification. Vavilov J. Genet. Breed. 26: 202–13. doi:10.18699/ VJGB-22-25.
Coombs, J., Long, S. P. and Hall, D. O. (1989). Techniques in bio productivity and photosynthesis, agropromizdat, Translated from English. Gudskov N. L. Editor: Mokronosov A. T. and Kovalev A. G., Мoskow. pp. 459.
Danilov, R., Ismailov, V., Tretyakov, V., Kremneva, O., Shumilov, Yu., Rizvanov, A., Krivoshein, V. and Kostenko I. (2018). Development of precision technologies of phytosanitary monitoring of agroecosystems on the basis of data of remote hyperspectral sensing of the earth. Achiev. Sci. Technol. Agro-indus. Complex 32: 82−86. doi:10.24411/0235-2451-2018-11019.
Danilov, R., Kremneva, O., Sereda, I., Gasiyan, K., Zimin M., Istomin D. and Pachkin A. (2024). Study of the spectral characteristics of crops of winter wheat varieties infected with pathogens of leaf plants. 13: doi:10.3390/plants13141892.
Firsov, N., Podlipnov, V., Ivliev, N., Nikolaev, P., Mashkov, S., Ishkin, P., Skidanov, R. and Nikonorov, A. (2021). Neural network-aided classification of hyperspectral vegetation images with a training sample generated using an adaptive vegetation index. Computer Optics 45: 887-96. doi:10.18287/2412-6179-CO-1038.
Gaidel, A. V., Podlipnov, V. V., Ivliev, N. A., Paringer, R. A., Ishkin, P. A., Mashkov, S. V. and Skidanov, R. V. (2023). Agricultural plant hyperspectral imaging dataset. Computer Optics 47: 442-50. doi:10.18287/2412-6179-CO-1226.
Kokhmetova, A. M., Rathan N. D., Sehgal, D., Malysheva, A., Kumarbayeva, M., Nurzhuma, M., Bolatbekova, A., Krishnappa, G., Gultyaeva, E., Kokhmetova, A., Keishilov, Z. and Bakhytuly, K. (2023). QTL mapping for seedling and adult plant resistance to stripe and leaf rust in two winter wheat populations. Front. Genet. 14: doi:10.3389/fgene.2023.1265859.
Kremneva, O. Yu., Danilov, R. Yu. and Ponomarev, A. (2023a). Spectral characteristics of winter wheat crops of the Bezostaya 100 variety affected by pathogens, Certificate of state registration of the database № RU 202362158 (In Russian).
Kremneva, O. Yu., Danilov, R. Yu., Gasiyan, K. and Ponomarev, A. (2023b) Spore-trapping device: an efficient tool to manage fungal diseases in winter wheat crops. Plants 126: doi:10.3390/plants12020391.
Kremneva, O. Yu., Tutubalina, O. V., Sereda, I. I., Danilov, R. Yu., Zimin, M. V. and Kurilov, A. A. (2020). Studies of changes in the spectral characteristics of winter wheat varieties depending on the degree of infection with pathogens. Current Problems in Remote Sensing of the Earth from Space 17: 149-61. doi:10.21046/2070-7401-2020-17-3-149-161.
Kudinova, O., Agapova, V., Volkova, G. and Kosman, E. (2023). Influence of biotic and abiotic factors on the virulence of Puccinia triticina population in southern Russia. Plant Pathol. 73: 404–18.
Lysov, A. K., Kornilov, T. V. and Khiutti, A. V. (2021). Spectral characteristics of reflection of waves in the optical range of healthy and diseased potato plants by Y-virus and late blight. Res. Crop. 22: 38-41.
Peterson, R. F., Cambell, A. B. and Hannah, A. E. (1948). A diagrammatic scale for estimating rust intensity on leaves and stems of cereals. Can. J. Res. 26c: 496–500.
Pinyasova, E. V. and Pavlova, E. V. (2024). Analysis of the distribution of the NDVI index on the arable land area of the Republic of Khakassia according to remote sensing data. Current Problems on Remote Sensing of the Earth from Space 21: 121−130. doi:10.21046/2070-7401-2024-21-3-121-130.
Siedliska, A., Baranowski, P., Pastuszka-Woźniak, J., Zubik M. and Krzyszczak, J. (2021). Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance. BMC Plant Biol. 21: 28−32. doi:10.1186/s12870-020-02807-4
Stuart, M. B., McGonigle, A. J. S. and Willmott, J. R. (2019). Hyperspectral imaging in environmental monitoring: A review of recent developments and technological advances in compact field deployable systems. Sensors 19: doi:10.3390/s19143071.
Trubitsyn, V. N. and Belik, M. A. (2018). Using a UAV when monitoring the condition of tall-stalker crops. Machinery and Equipment for Rural Area 1: 30−32.
Yakushev, V. P., Kanash, E. V., Rusakov, D. V., Yakushev, V. V. and Blokhina, S. Yu. (2022). Correlation dependences between crop reflection indices, grain yield and optical characteristics of wheat leaves at different nitrogen level and seeding density. Agric. Biol. 57: 98-112. doi:10.15389/agrobiology.2022.1.98rus.
Yakushev, V. P., Yakushev, V. V., Blokhina, S. Yu., Blokhin, Yu. I. and Matveenko, D. A. (2023). The role of remote sensing of the Earth in precision agriculture. Herald of the Russian Academy of Sciences. 93: 955-69. doi:10.31857/S0869587323100110.
Anpilogova, L. K. and Volkova, G. V. (2000). Methods of creating artificial infectious backgrounds and evaluating wheat varieties for resistance to harmful diseases (fusarium head blight, rust, powdery mildew), VNIIBZR, RASKHN, Krasnodar. pp: 28.
Avinash, P., Ramathilaga, A. and Valarmathi, P. (2022). Hyperspectral remote sensing for discrimination for plant disease forecasting: Review. J. Pharmacogn. Phytochem. 11: 208–15.
Chengzhi, C., Jidong, C. and Wenfang, C. (2024). Potential yields of two staple cereal crops worldwide under global warming. Farm. Manage. 9: 1-11.
Cheshkova, A. F. (2022). A review of hyperspectral image analysis techniques for plant disease detection and identification. Vavilov J. Genet. Breed. 26: 202–13. doi:10.18699/ VJGB-22-25.
Coombs, J., Long, S. P. and Hall, D. O. (1989). Techniques in bio productivity and photosynthesis, agropromizdat, Translated from English. Gudskov N. L. Editor: Mokronosov A. T. and Kovalev A. G., Мoskow. pp. 459.
Danilov, R., Ismailov, V., Tretyakov, V., Kremneva, O., Shumilov, Yu., Rizvanov, A., Krivoshein, V. and Kostenko I. (2018). Development of precision technologies of phytosanitary monitoring of agroecosystems on the basis of data of remote hyperspectral sensing of the earth. Achiev. Sci. Technol. Agro-indus. Complex 32: 82−86. doi:10.24411/0235-2451-2018-11019.
Danilov, R., Kremneva, O., Sereda, I., Gasiyan, K., Zimin M., Istomin D. and Pachkin A. (2024). Study of the spectral characteristics of crops of winter wheat varieties infected with pathogens of leaf plants. 13: doi:10.3390/plants13141892.
Firsov, N., Podlipnov, V., Ivliev, N., Nikolaev, P., Mashkov, S., Ishkin, P., Skidanov, R. and Nikonorov, A. (2021). Neural network-aided classification of hyperspectral vegetation images with a training sample generated using an adaptive vegetation index. Computer Optics 45: 887-96. doi:10.18287/2412-6179-CO-1038.
Gaidel, A. V., Podlipnov, V. V., Ivliev, N. A., Paringer, R. A., Ishkin, P. A., Mashkov, S. V. and Skidanov, R. V. (2023). Agricultural plant hyperspectral imaging dataset. Computer Optics 47: 442-50. doi:10.18287/2412-6179-CO-1226.
Kokhmetova, A. M., Rathan N. D., Sehgal, D., Malysheva, A., Kumarbayeva, M., Nurzhuma, M., Bolatbekova, A., Krishnappa, G., Gultyaeva, E., Kokhmetova, A., Keishilov, Z. and Bakhytuly, K. (2023). QTL mapping for seedling and adult plant resistance to stripe and leaf rust in two winter wheat populations. Front. Genet. 14: doi:10.3389/fgene.2023.1265859.
Kremneva, O. Yu., Danilov, R. Yu. and Ponomarev, A. (2023a). Spectral characteristics of winter wheat crops of the Bezostaya 100 variety affected by pathogens, Certificate of state registration of the database № RU 202362158 (In Russian).
Kremneva, O. Yu., Danilov, R. Yu., Gasiyan, K. and Ponomarev, A. (2023b) Spore-trapping device: an efficient tool to manage fungal diseases in winter wheat crops. Plants 126: doi:10.3390/plants12020391.
Kremneva, O. Yu., Tutubalina, O. V., Sereda, I. I., Danilov, R. Yu., Zimin, M. V. and Kurilov, A. A. (2020). Studies of changes in the spectral characteristics of winter wheat varieties depending on the degree of infection with pathogens. Current Problems in Remote Sensing of the Earth from Space 17: 149-61. doi:10.21046/2070-7401-2020-17-3-149-161.
Kudinova, O., Agapova, V., Volkova, G. and Kosman, E. (2023). Influence of biotic and abiotic factors on the virulence of Puccinia triticina population in southern Russia. Plant Pathol. 73: 404–18.
Lysov, A. K., Kornilov, T. V. and Khiutti, A. V. (2021). Spectral characteristics of reflection of waves in the optical range of healthy and diseased potato plants by Y-virus and late blight. Res. Crop. 22: 38-41.
Peterson, R. F., Cambell, A. B. and Hannah, A. E. (1948). A diagrammatic scale for estimating rust intensity on leaves and stems of cereals. Can. J. Res. 26c: 496–500.
Pinyasova, E. V. and Pavlova, E. V. (2024). Analysis of the distribution of the NDVI index on the arable land area of the Republic of Khakassia according to remote sensing data. Current Problems on Remote Sensing of the Earth from Space 21: 121−130. doi:10.21046/2070-7401-2024-21-3-121-130.
Siedliska, A., Baranowski, P., Pastuszka-Woźniak, J., Zubik M. and Krzyszczak, J. (2021). Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance. BMC Plant Biol. 21: 28−32. doi:10.1186/s12870-020-02807-4
Stuart, M. B., McGonigle, A. J. S. and Willmott, J. R. (2019). Hyperspectral imaging in environmental monitoring: A review of recent developments and technological advances in compact field deployable systems. Sensors 19: doi:10.3390/s19143071.
Trubitsyn, V. N. and Belik, M. A. (2018). Using a UAV when monitoring the condition of tall-stalker crops. Machinery and Equipment for Rural Area 1: 30−32.
Yakushev, V. P., Kanash, E. V., Rusakov, D. V., Yakushev, V. V. and Blokhina, S. Yu. (2022). Correlation dependences between crop reflection indices, grain yield and optical characteristics of wheat leaves at different nitrogen level and seeding density. Agric. Biol. 57: 98-112. doi:10.15389/agrobiology.2022.1.98rus.
Yakushev, V. P., Yakushev, V. V., Blokhina, S. Yu., Blokhin, Yu. I. and Matveenko, D. A. (2023). The role of remote sensing of the Earth in precision agriculture. Herald of the Russian Academy of Sciences. 93: 955-69. doi:10.31857/S0869587323100110.
