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The use of unmanned aerial vehicles (UAVs) is booming in almost every sector of the economy, especially in the agricultural industry. According to some reports, the agricultural UAV market is expected to increase from USD 2.6 billion in 2020 to USD9.5 billion in 2030. In this paper a brief overview devoted to the use of UAVs in the Russian State Agrarian University - Moscow Timiryazev Agricultural Academy (RSAU-MTAA), including the results of studying the equipment use effectiveness for automatic driving of tractor equipment when sowing grain crops and planting potatoes. In the course of studying the equipment use effectiveness for automatic driving of tractor equipment, the deviations of the guess row spacing from the standard row spacing provided for by the seeder design were established; in the case of sowing barley using a marker, it was up to 4.3 cm, and in the case of winter wheat it was up to 5 cm. When using the autopilot system, these values were no more than 1.5 and 2.3 cm, respectively, which indicates the high accuracy and efficiency of the automatic driving systems. The autopilot system use provided a deviation of adjacent rows from the straightness when planting potatoes from 2.8 to 3.0 cm. The paper concludes that the use of unmanned robotic systems in agriculture, in conjunction with modern means of receiving and processing information, opens up new opportunities for increasing agriculture efficiency.
Wydawca
Czasopismo
Rocznik
Tom
Strony
219--223
Opis fizyczny
Bibliogr. 21 poz., tab.
Twórcy
autor
- Federal State Budgetary Educational Institution of Higher Education “Russian State Agrarian University – Moscow Timiryazev Agricultural Academy”, Reclamation and Construction Machines Department, Timiryazevskaya street, 49, Moscow, 127550, Russia
autor
- Federal State Budgetary Educational Institution of Higher Education “Russian State Agrarian University – Moscow Timiryazev Agricultural Academy”, A.N. Kostyakov Institute of Land Reclamation, Water Management and Construction
autor
- Federal State Budgetary Educational Institution of Higher Education “Russian State Agrarian University – Moscow Timiryazev Agricultural Academy”, A.N. Kostyakov Institute of Land Reclamation, Water Management and Construction
Bibliografia
- AKHMEDYAROV Y. 2019. Agricultural market digitalization in Kazakhstan. Economics. Ecology. Socium. Vol. 3(4) p. 1–9. DOI 10.31520/2616-7107/2019.3.4-1.
- AFSHAR A., SOLEIMANIAN E., AKBARI VARIANI H., VAHABZADEH M., MOLAJOU A. 2021. The conceptual framework to determine interrelations and interactions for holistic Water, Energy, and Food Nexus. Environment, Development and Sustainability. DOI 10.1007/s10668-021-01858-3.
- AUBERT B.A., SCHROEDER A., GRIMAUDO J. 2012. IT as enabler of sustainable farming: An empirical analysis of farmers' adoption decision of precision agriculture technology. Decision Support Systems. Vol. 54(1) p. 510–520. DOI 10.1016/j.dss.2012.07.002.
- BLACKMORE S. 1994. Precision farming: an introduction. Outlook on Agriculture. Vol. 23(4) p. 275–280. DOI 10.1177/003072709402300407.
- CHAO C.C., YANG J.M., JEN W.Y. 2007. Determining technology trends and forecasts of RFID by a historical review and bibliometric analysis from 1991 to 2005. Technovation. Vol. 27(5) p. 268–279. DOI 10.1016/j.technovation.2006.09.003.
- EL BILALI H., ALLAHYARI M.S. 2018. Transition towards sustainability in agriculture and food systems: Role of information and communication technologies. Information Processing in Agriculture. Vol. 5(4) p. 456–464. DOI 10.1016/j.inpa.2018.06.006.
- EVE M.D., SPEROW M., PAUSTIAN K., FOLLETT R.F. 2002. National-scale estimation of changes in soil carbon stocks on agricultural lands. Environmental Pollution. Vol. 116(3) p. 431–438. DOI 10.1016/s0269-7491(01)00220-2.
- FUNG K.F., HUANG Y.F., KOO C.H., SOH Y.W. 2020. Drought forecasting: A review of modelling approaches 2007–2017. Journal of Water and Climate Change. Vol. 11(3) p. 771–799. DOI 10.2166/wcc.2019.236.
- GUSEV A.S., BEZNOSOV G.A., ZIABLITCKAIA N.V., KHOLMANSKIKH M.V., NOVOPASHIN L.A., DENYOZHKO L.V., SADOV A.A. 2019. An analysis of research areas in precision agriculture. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Vol. 10(12), 10A12D p. 1–10. DOI 10.14456/ITJEMAST.2019.154.
- KEICHER R., SEUFERT H. 2000. Automatic guidance for agricultural vehicles in Europe. Computers and Electronics in Agriculture. Vol. 25(1–2) p. 169–194. DOI 10.1016/S0168-1699(99)00062-9.
- KIM J., KIM S., JU C., SON H.I. 2019. Unmanned aerial vehicles in agriculture: A review of perspective of platform, control, and applications. IEEE Access. Vol. 7 p. 105100–105115. DOI 10.1109/ACCESS.2019.2932119.
- MALOKU D., BALOGH P., BAI A., GABNAI Z., LENGYEL P. 2020. Trends in scientific research on precision farming in agriculture using science mapping method. International Review of Applied Sciences and Engineering. Vol. 11(3) p. 232–242. DOI 10.1556/1848.2020.00086.
- MOLAJOU A., AFSHAR A., KHOSRAVI M., SOLEIMANIAN E., VAHABZADEH M., VARIANI H.A. 2021a. A new paradigm of water, food, and energy nexus. Environmental Science and Pollution Research. Vol. 2021. DOI 10.1007/s11356-021-13034-1.
- MOLAJOU A., POULADI P., AFSHAR A. 2021. Incorporating social system into water-food-energy nexus. Water Resources Management. Vol. 35(13) p. 4561–4580. DOI 10.1007/s11269-021-02967-4.
- OZDOGAN B., GACAR A., AKTAS H. 2017. Digital agriculture practices in the context of agriculture 4.0. Journal of Economics Finance and Accounting. Vol. 4(2) p. 186–193. DOI 10.17261/Pressacade-mia.2017.448.
- REN G., LIN T., YING Y., CHOWDHARY G., TING K.C. 2020. Agricultural robotics research applicable to poultry production: A review. Computers and Electronics in Agriculture. Vol. 169, 105216. DOI 10.1016/j.compag.2020.105216.
- SHI X., AN X., ZHAO Q., LIU H., XIA L., SUN X., GUO Y. 2019. State-of-the-art internet of things in protected agriculture. Sensors. Vol. 19(8), 1833. DOI 10.3390/s19081833.
- TISHKINA S.N., ALKHASOV T.G., LUKYANTSEVA D.V., BEZDENEZHNYKH T.P. 2019. Rossiyskiy opyt ispol'zovaniya podkhodov k raschetu potrebnosti vo vrachebnykh kadrakh [Approaches to assessing the demand for medical personnel in the Russian Federation]. Farmakoekonomika. Sovremennaya Farmakoekonomika i Farmakoepidemiologiya / FARMAKOEKONOMIKA. Modern Pharmacoeconomic and Pharmacoepidemiology. Vol. 12(3) p. 230–238. DOI 10.17749/2070-4909.2019.12.3.230-238.
- WORTMANN F., FLÜCHTER K. 2015. Internet of things. Business & Information Systems Engineering. Vol. 57(3) p. 221–224. DOI 10.1007/s12599-015-0383-3.
- ZEYLIGER A.M., ERMOLAEVA O.S., MUZYLEV E.L., STARTSEVA Z.P., SUKHAREV Y.I. 2019. Komp’yuternyy analiz rezhimov odnogo stressa oroshayemykh agrotsenozov s ispol’zovaniyem SWAP-modeli, a takzhe dannykh nazemnogo i kosmicheskogo monitoringa [Computer analysis of water stress regimes of an irrigated agrocoenosis using the SWAP model and ground and space monitoring data]. Computer. Vol. 16(3) p. 33–43. DOI 10.21046/2070-7401-2019-16-3-33-43.
- ZHAO C., LI J., FENG X., GUO M. 2018. Application status and trend of “Internet Plus” Modern Agriculture in China and Abroad. Strategic Study of Chinese Academy of Engineering. Vol. 20(2) p. 50–56. DOI 10.15302/J-SSCAE-2018.02.008.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-340bb4e1-1a32-4c47-9301-7b117d529b1e