Regarding some of the methods for crop state calculation in digital twin of plant. Part 2

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Abstract

In this paper, a concept of digital twin of plant, which is a decision support system to implement precise farming technologies. Digital twin of plant allows to forecast and simulate real crop state and suggest agricultural measures to the fields based on weather and soil data. Digital twin of plant is developed with the use of multi-agent technologies and ontology-based domain formalization.

About the authors

Petr O. Skobelev

Samara State Technical University; Samara Federal Research Center of Russian Academy of Sciences

Author for correspondence.
Email: petr.skobelev@gmail.com

D.T.Sc., Chief Researcher of “Digital Twins of Plants” Lab at Samara Federal Research Center of RAS, Head of Department “Electronical Systems and Information Security” at Samara State Technical University

Russian Federation, Samara

Aleksey S. Tabachinskiy

Samara State Technical University; Samara Federal Research Center of Russian Academy of Sciences

Email: tabachinski.as@samgtu.ru

PhD, Head of “Digital Twins of Plants” Lab at Samara Federal Research Center of RAS, Associate Professor and Researcher of “Theoretical and Basic Electrical Engineering” Department at Samara State Technical University 

Russian Federation, Samara

Elena V. Simonova

Samara National Research University

Email: simonova@smartsolutions-123.ru

PhD, Associate Professor of Information Systems and Technologies Department at Samara University

Russian Federation, Samara

Yulia N. Zhuravel

Rocket Space Center “Progress”

Email: ntsomz_5@mail.ru

Lead Specialist at Rocket Space Center “Progress” 

Russian Federation, Samara

Gennady N. Myatov

Samara State Technical University

Email: miatov@mail.ru

D.T.Sc, Professor of Electrical Drives and Automation Department at Samara State Technical University

Russian Federation, Samara

References

  1. Zahn, M. Systems to prescribe and deliver fertilizer over agricultural fields and related methods, 2019, US Patent 20190043142.
  2. Bittner, P. Devices and methods for planning and monitoring agricultural crop growing, 2017, WO Patent 2017/148818.
  3. Wang, G. Agricultural-big-data-based method and apparatus for generating growing progress of crops, 2016, CN Patent 106530107.
  4. Zhang, C. 2019, Cross-scale high-precision dynamic crop growth monitoring and yield assessment method based on high-resolution remote sensing data and a crop model, Patent № CN109829234.
  5. Fujiama, K. Information processing device, information processing method, and recording medium on which information processing program is recorded, 2018, WO Patent 2018/131480.
  6. Budaev, D., Lada, A., Simonova, E., Skobelev, P., Travin, V., Yalovenko, O., Voshchuk, G., Zhilyaev, A. Conceptual design of smart farming solution for precise agriculture // Rzevski, G., Syngellakis, S. (Eds.). Management and Applications of Complex Systems. - WIT Press. 2019. - P. 139-146.
  7. Skobelev, P.O. Realizatsiya tsifrovogo dvoynika rasteniy dlya adaptivnogo rascheta dlitel'nosti stadiy razvitiya i prognozirovaniya urozhaynosti kul'tur v kiber-fizicheskoy sisteme upravleniya tochnym zemledeliem / P.O. Skobelev, I.V. Mayorov, E.V. Simonova, O.I. Goryanin, A.A. Zhilyaev, A.S. Tabachinskiy, V.V. Yalovenko // Sbornik trudov XXXIII Mezhdunarodnoy nauchnoy konferentsii Matematicheskie metody v tekhnike i tekhnologiyakh - MMTT-33, 14-18 sentyabrya, 2020, Kazan'. - Matematicheskie metody v tekhnike i tekhnologiyakh: sb. tr. mezhdunar. nauch. konf.: v 12 t. T. 12 chast' 3 / pod obshch. red. A. A. Bol'shakova. - SPb.: Izd-vo Politekhn. un-ta, 2020. - C. 133-136. EDN: UGJYEK
  8. Petr Skobelev, Vladimir Larukchin, Elena Simonova, Oleg Goryanin, Olga Yalovenko, Vladimir Yalovenko. Developing a smart cyber-physical system based on digital twins of plants // Proceedings of the Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WORLDS4 2020), 27-28 July, 2020, London, United Kingdom. - IEEE, IEEE Xplore® Digital Library. - P. 522 - 527. - https://ieeexplore.ieee.org/xpl/conhome/9203790/proceeding. EDN: KANBDA
  9. Goryanin O.I. Klimat i ego vliyanie na produktivnost' polevykh kul'tur v srednem Zavolzh'e. Rekomendatsii / O.I. Goryanin - Samarskiy nauchno-issledovatel'skiy institut sel'skogo khozyaystva im. N.M. Tulaykova. - Samara, 2018. - 28 s. EDN: IJYQNG
  10. Skobelev, P., Mayorov, I., Simonova, E., Goryanin, O., Zhilyaev, A., Tabachinskiy, A., Yalovenko, V. Development of models and methods for creating a digital twin of plant within the cyber-physical system for precision farming management. J. Phys.: Conf. Ser. 2020, 1703, 012022. EDN: YDISSV

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Copyright (c) 2023 Skobelev P.O., Tabachinskiy A.S., Simonova E.V., Zhuravel Y.N., Myatov G.N.

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