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All issues Volume 285 (2021) E3S Web Conf., 285 (2021) 02038 References
Open Access
Issue
E3S Web Conf.
Volume 285, 2021
International Conference on Advances in Agrobusiness and Biotechnology Research (ABR 2021)
Article Number 02038
Number of page(s) 11
Section Agronomy and Crop Sciences
DOI https://doi.org/10.1051/e3sconf/202128502038
Published online 06 July 2021
  1. S. Skubiev, D. Shapovalov, P. Lepekhin Experience of using UAVs for monitoring the state of rice crops in the Krasnodar Territory // Rice journal 4(41) (2018). [Google Scholar]
  2. J. Barbedo A Review on the Use of Unmanned Aerial Vehicles // Drones 3(40) (2019). [CrossRef] [Google Scholar]
  3. N. Kurchenko, Ya. Ilchenko, E. Truflyak Development of software for processing images obtained from unmanned aerial vehicles. // KubSAU (2019). [Google Scholar]
  4. M. González-Betancourt, M., Z.L Mayorga-Ruíz. Normalized difference vegetation index for rice management in El Espinal, Colombia // DYNA. 85 (2018) [Electronic resource]. URL: https://www.researchgate.net/publication/325406776_Normalized_difference_vegetation_index_for_rice_management_in_El_Espinal_Colombia [Google Scholar]
  5. Y. Seo, U. Shotaro Evaluating Farm Management Performance by the Choice of PestControl Sprayers in Rice Farming in Japan // Sustainability 13 (5) (2021) [Google Scholar]
  6. Drones used in rice farming in central Vietnam // [Electronic resource] URL: https://vietnamnet.vn/en/sci-tech-environment/drones-now-used-by-vietnam-s-ricefarmers-595666.html (2020). [Google Scholar]
  7. Yu. A. Lysenko, I.N. Chuev, V.A. Khrisonid Problems and prospects of rice growing on the example of the Krasnodar Territory and the Republic of Adygea // Fundamental Research 4 (2019). [Google Scholar]
  8. In the Kuban, the rice harvest was kept at the level of 2019 [Electronic resource] URL: https://tass.ru/ekonomika/9882401 [Google Scholar]
  9. O.A. Gutorova, A.Kh. Sheudzhen Ecological and agrochemical state of soils in rice agrolandscapes: monograph. Maykop: (JSC “Polygraph-Yug”, 2020). [Google Scholar]
  10. S.A. Vladimirov, P.P. Meshcheryakov. Implementation of the bioclimatic productivity of the rice field // International scientific journal “Innovative Science” 1 (2018) [Google Scholar]
  11. X. Zhou, H.B. Zheng, X.Q. Xu Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery // ISPRS Journal of Photogrammetry and Remote Sensing. 130 (2017) DOI:10.1016/j.isprsjprs.2017.05.003 [Google Scholar]
  12. Carlos A. Devia, Juan P. Rojas, T. Petro et al. High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery // Journal of Intelligent & Robotic Systems 96, 11 (2019) DOI: 10.1007/s10846-019-01001-5 [Google Scholar]
  13. Bo Duan, S. Fang, R. Zhu, X. Wu et al. Remote Estimation of Rice Yield with Unmanned Aerial Vehicle (UAV) Data and Spectral Mixture Analysis // Front. Plant Sci. Feb (2019) [Google Scholar]
  14. Qi Yang L. Shu,. J. Hun. et. al. A near real-time deep learning approach for detecting rice phenology based on UAV images // Agricultural and Forest Meteorology 287 (2020). https://doi.org/10.1016/j.agrformet.2020.107938 [Google Scholar]
  15. Sourav Kumar Bhoi, K.K. Jena, H.V. Long et. al. An Internet of Things assisted Unmanned Aerial Vehicle based artificial intelligence model for rice pest detection // Microprocessors and Microsystems Feb 80 (2021) https://doi.org/10.1016/j.micpro.2020.103607 [Google Scholar]
  16. Russia A.ts.m. The use of spatial data for the formation of state information resources on agricultural lands 2019. URL: https://pd.gosreforma.ru/wpcontent/uploads/2019/11/ReportoftheAnalyticalCenteroftheMinistryofAgricultureofRussia.pdf (2019) [Google Scholar]
  17. V.A. Popov, N.V. Ostrovsky Agroclimatologists and Rice Ecosystem Hydraulics. Krasnodar: KubGAU, 2013.189 p. [Google Scholar]
  18. N.R Magomedov. Resource-saving herbicide-free technology of rice cultivation in Dagestan // Rice growing. 2019. No. 2(43). p. 57-60. [Google Scholar]
  19. S.A. Vladimirov, I.A. Prikhodko, A.Yu Verbitsky Improving the methods of irrigation of rice and crop rotation crops // Eurasian Union of Scientists (ESU) 4(61) (2019) DOI: 10.31618/ESU.2413-9335.2019.7.61.63 [Google Scholar]
  20. S.A. Vladimirov, S.V. Derkachev, A.S. Bezridny Planning and irrigation regime as factors of increasing the potential of the rice check // Collection of articles of the international scientific-practical conference: in 3 parts. Kazan. (2017) [Google Scholar]
  21. F.A. Alekseenko, A.S. Bezridny, A.S. Vladimirov International scientific journal “Symbol of Science” 4 (2016) [Google Scholar]
  22. G. Balakay, L. Dokuchaeva, R. Yurkova The problem of rate development of rice water consumption and water disposal from rice irrigation systems, Scientific journal of the Russian Research Institute of Melioration Problems, No. 3(31), (2018) [Google Scholar]
  23. N. Ostrovskiy, V. Ostrovskiy, V. Shiskin Bull. Of the Nizhnevartovskiy agrouniversity complex: scince and higer professional education, No 3(47) (2017) [Google Scholar]
  24. S.V. Garkusha, M.A. Skazhennik, E.N. Kiselev, V.N Chizhikov and A.F. Petrushi Research of rice crops in Krasnodar region by remote sensing date. E3S Web of Conferences 175, 01004 (2020) INTERAGROMASH (2020) [EDP Sciences] [Google Scholar]
  25. Aidil P.P. Rizky, Mohamad Solahudin Liyantono Multi-copter development as a tool to determine the fertility of rice plants in the vegetation phase using aerial photos // Procedia Environmental Sciences 24. (2015) [Google Scholar]
  26. S. Huang, Y. Miao, F. Yuan, X. Ma, X. et al. Satellite remote sensing-based in-season diagnosis of rice nitrogen status in Northeast China. Remote Sens. 7, 10646-10667. DOI:10.3390/rs70810646 (2015) [Google Scholar]
  27. J. Lu, Y. Miao, W. Shi, et al. Evaluating different approaches to non-destructive nitrogen status diagnosis of rice using portable RapidSCAN active canonpy sensor. Sci. Rep. 7:14073. DOI:10.1038/s41598-017-14597-1 (2017) [CrossRef] [PubMed] [Google Scholar]

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