DEVELOPMENT OF INTELLIGENT INTEGRATED SYSTEM FOR "SMART" AGRICULTURAL PRODUCTION

  • Z.V. Nagoev Kabardin-Balkar Scientific Center of the Russian Academy of Sciences
  • V. М. Shuganov Kabardin-Balkar Scientific Center of the Russian Academy of Sciences
  • А.U. Zammoev Kabardin-Balkar Scientific Center of the Russian Academy of Sciences
  • К. C. Bzhikhatlov Kabardin-Balkar Scientific Center of the Russian Academy of Sciences
  • Z.Z. Ivanov Kabardin-Balkar Scientific Center of the Russian Academy of Sciences
Keywords: Agricultural production, digital agricultural production, precision farming, robotization, automation of agricultural production, intelligent integrated system

Abstract

The production of agricultural goods is currently associated with the use of digital technologies,
elements of precision farming, automation and robotization of agriculture. These technologies
make it possible to carry out continuous monitoring, carry out timely processing, improve the
efficiency of production and use of resources. The need for the integrated use of digital technologies
and artificial intelligence and the creation of intelligent integrated systems for agricultural
production is noted. Studies show that IT-technologies are actively used in field farming when
growing grain crops. The main crop in the production of breeding, seed and commercial grain in
the Kabardino-Balkarian Republic is corn, so it is assumed that the intelligent system of the "smart
field" should be developed initially for this particular crop, and then, with some modifications,
used for the production of any crop products – other types of grain, vegetables, fruits, grapes and
gourds. It allows you to reduce human participation at some stages of production by automating
the process and controlling it through various "smart" devices. The operation of the "smart field"
system is based on the use of a variety of sensors, including those installed on mobile equipment
(ground and air manned and unmanned vehicles, space satellites) and portable portable devices to
obtain operational data on the state of fields and crops. This allows: – analyze the readiness of
agricultural land for sowing, monitor the progress of plant vegetation in order to effectively and
efficiently plan agrotechnical measures (chemical protection against pests and diseases, fertilizing,
irrigation, etc.); – predict production efficiency indicators (total gross harvest, yield per hectare), as well as timely identify production risks (appearance of pests, plant diseases, soil salinity,
etc.). – make effective decisions on managing the use of resources of agricultural enterprises. With
the use of "smart" devices, it became possible to introduce the so-called. "precision farming" to
manage crop productivity, taking into account changes in the plant habitat. Ultimately, this makes
it possible to solve two main tasks of agricultural producers - increasing yields and reducing
costs. The authors have developed the concept of an intelligent integrated system "Smart Field" for
the production of corn grain using advanced robotic systems and complexes. The architecture of
the "Smart Field" system for the production of seed and commercial corn is presented, which can
be adapted with minor modifications for the production of other crop products.

References

1. Appaev S., Kagermazov A., Khachidogov A., Bizhoev M., Khatefov E. Development of selfpollinated
maize lines based on the teosinte collection of the N.I. Vavilov institute of plant industry
(VIR), E3S Web of Conferences, 2021, 262, 01010. DOI: https://doi.org/10.1051/
e3sconf/202126201010.
2. Boyko V.N., Khatefov E.B. Iskhodnyy material dlya gibridnoy selektsii kukuruzy na
mnogopochatkovost' iz kollektsii VIR [Source material from the VIR collection for hybrid
breeding of multiple-ear maize], Tr. po prikladnoy botanike, genetike i selektsii [Proceedings
on Applied Botany, Genetics and Breeding], 2021, Vol. 182 (4), pp. 27-35. DOI:
https://doi.org/10.30901/2227-8834-2021-4-27-35.
3. Dushyant Kumar Singh, Rajeev Sobti. Long-range real-time monitoring strategy for Precision
Irrigation in urban and rural farming in society 5.0, Computers & Industrial Engineering,
2022, Vol. 167, 107997. DOI: https://doi.org/10.1016/j.cie.2022.107997.
4. Manschadi A.M., Palka M., Fuchs W., Neubauer T., Eitzinger J., Oberforster M., Soltani A.
Performance of the SSM-iCrop model for predicting growth and nitrogen dynamics in winter
wheat, European Journal of Agronomy, 2022, Vol. 135, 126487. DOI: https://doi.org/
10.1016/j.eja.2022.126487.
5. Deepika Sarpal, Raka Sinha, Madhavi Jha, Padmini. TN AgriWealth: IoT based farming system,
2022, Vol. 89, 104447. DOI: https://doi.org/10.1016/j.micpro.2022.104447.
6. Fedotova G.V., Gorlov I.F., Slozhenkina M.I., Glushchenko A.V. Trendy nauchnotekhnicheskogo
razvitiya i povysheniya konkurentospsobnosti sel'skogo khozyaystva Rossii
[Trends in scientific and technological development and increasing the competitiveness of Russian agriculture], Vestnik Akademii znaniy [Bulletin of the Academy of Knowledge], 2019,
No. 3 (32). Available at: https://cyberleninka.ru/article/n/trendy-nauchno-tehnicheskogorazvitiya-
i-povysheniya-konkurentospsobnosti-selskogo-hozyaystva-rossii.
7. Moskalev S.M., Klimenok-Kudinova N.V. Iskusstvennyy intellekt i internet veshchey kak
innovatsionnye metody sovershenstvovaniya agropromyshlennogo sektora [Artificial Intelligence
and the Internet of Things as Innovative Methods for Improving the Agro-Industrial
Sector], Izvestiya SPbGAU [Izvestiya SPbGAU], 2018, No. 3 (52). Available at:
https://cyberleninka.ru/article/n/iskusstvennyy-intellekt-i-internet-veschey-kakinnovatsionnye-
metody-sovershenstvovaniya-agropromyshlennogo-sektora.
8. Nagoev Z.V., Shuganov V.M., Bzhikhatlov K.Ch., Zammoev A.U., Ivanov Z.Z. Perspektivy
povysheniya proizvoditel'nosti i effektivnosti sel'skokhozyaystvennogo proizvodstva s
primeneniem intellektual'noy integrirovannoy sredy [Prospects for increasing the productivity
and efficiency of agricultural production using an intelligent integrated environment], Izvestiya
KBNTS RAN [Proceedings of the Kabardino-Balkarian Scientific Center of the Russian Academy
of Sciences], 2021, No. 6.
9. Torikov V.E., Pogonyshev V.A., Pogonysheva D.A., Dornykh G.E. Sostoyanie tsifrovoy
transformatsii sel'skogo khozyaystva [State of digital transformation of agriculture], Vestnik
Kurskoy gosudarstvennoy sel'skokhozyaystvennoy akademii [Bulletin of the Kursk State Agricultural
Academy], 2020, No. 9. Available at: https://cyberleninka.ru/article/n/sostoyanietsifrovoy-
transformatsii-selskogo-hozyaystva.
10. IT v agropromyshlennom komplekse Rossii. Internet-resurs [IT in the agro-industrial complex
of Russia]. Available at: https://www.tadviser.ru/index.php/Stat'ya:IT_v_agropromysh
lennom_komplekse_Rossii.
11. Zavriev S.K., Ignatov A.N. Potential threats in agriculture and food security area, World Economy
and International Relations, 2020, Vol. 64 (7), pp. 100-107. DOI: 10.20542/0131-2227-
2020-64-7-100-107.
12. Rosa A.T., Creech C.F., Elmore, R.W. and others. Implications of cover crop planting and
termination timing on rainfed maize production in semi-arid cropping systems, Field Crops
Research, 2021, 271, 108251. DOI: https://doi.org/10.1016/j.fcr.2021.108251.
13. Carabajal-Capitán S., Kniss A.R., Jabbour R. Seed Predation of Interseeded Cover Crops and
Resulting Impacts on Ground Beetles, Proceedings of the National Academy of Sciences of the
United States of America, 2021, Vol. 118 (18), 2017470118. DOI: https://doi.org/10.1073/
pnas.2017470118.
14. Gordeev A.V. i dr. Vedomstvennyy proekt «Tsifrovoe sel'skoe khozyaystvo» [Departmental
project "Digital Agriculture"]. Moscow: FGBNU «Rosinformagrotekh», 2019, 48 p.
15. Anishchenko Alesya Nikolaevna, Shut'kov Anatoliy Antonovich. Agriculture 4. 0 kak
perspektivnaya model' nauchno-tekhnologicheskogo razvitiya agrarnogo sektora sovremennoy
Rossii [Agriculture 4. 0 as a promising model of scientific and technological development of
the agricultural sector of modern Russia], Prodovol'stvennaya politika i bezopasnost' [Food
Policy and Security], 2019, No. 3. Available at: https://cyberleninka.ru/article/n/agriculture-4-
0-kak-perspektivnaya-model-nauchno-tehnologicheskogo-razvitiya-agrarnogo-sektorasovremennoy-
rossii.
16. Tzounis A. et al. Internet of Things in agriculture, recent advances and future challenges,
Biosystems engineering, 2017, Vol. 164, pp. 31-48.
17. ESP32-WROOM-32 Datasheet. Available at: https://www.espressif.com/sites/default/files/
documentation/esp32-wroom-32_datasheet_en.pdf.
18. LoRaWAN Specification v1.1. 2017. Интернет-ресурс. Available at: https://loraalliance.
org/wp-content/uploads/2020/11/lorawantm_specification_-v1.1.pdf.
19. Nagoev Z., Pshenokova I., Nagoeva O., Sundukov Z. Learning algorithm for an intelligent
decision making system based on multi-agent neurocognitive architectures, Cognitive Systems
Research. Elsevier, 2021, Vol. 66, pp. 82-88.
20. Nagoev Z.V., Denisenko V.A., Lyutikova L.A. System of autonomous robot machine vision for
agricultural application in mountain territories based on the multi-agent cognitive architectures,
Sustainable Development of Mountain Territories, 2018, No. 10 (2), pp. 289-297.
21. Nagoev Z.V. Multiagent recursive cognitive architecture, Mechanics of Solids, 2014, Vol. 46
(4), pp. 622-634. DOI: https://doi.org/10.1007/978-3-642-34274-5_43.
Published
2022-04-21
Section
SECTION I. PROSPECTS FOR THE USE OF ROBOTIC SYSTEMS