MULTI-AGENT ALGORITHM FOR COLLECTING DATA FROM WEATHER STATION FOR FORECASTING PRODUCTIVITY AND CROPS CONDITION
Keywords:
Intellectual agent, multi-agent algorithm, neurocognitive architecture, weather station, intelligent system, smart farming
Abstract
The weather affects the productivity and condition of crops, the requirements for the quantity
and quality of fertilizers, as well as preventive measures to prevent diseases. Bad weather can
affect the quality of products during transportation and storage, and hence the germination of
seeds and planting material. Various intelligent monitoring systems are now widely used in agriculture,
which include satellite monitoring and weather stations. In this case, the choice of a
method for analyzing the received data and intelligent systems for their processing for predictive
forecasting plays a fundamental role. The purpose of this study is to develop an intellectual system
for predicting the state of the crop based on data from a weather station. A multi-agent algorithm
for predicting the state of crops according to data from a weather station based on the selforganization
of neurocognitive architecture was developed in this study. The description of the
block diagram of the weather station and its sensors is given. A program algorithm has been developed
for collecting and processing data from weather station sensors. As a result of processing,
data on air and soil temperature, air and soil humidity, wind speed and direction, precipitation
amount and the sum of active temperatures are sent to the intelligent decision-making system. A
system for constructing cause-and-effect relationships is described. This system can make recommendations
or forecasts on the condition of the crop and on the likelihood of diseases and pests in
controlled crops.
References
1. Stepanov A.S. Prognozirovanie urozhaynosti sel'skokhozyaystvennykh kul'tur na osnove dannykh
distantsionnogo zondirovaniya Zemli (na primere soi) [Forecasting crop yields based on Earth remote
sensing data (on the example of soybeans], Vychislitel'nye tekhnologii [Computational technologies],
2019, Vol. 24, No. 6, pp. 125-133. DOI: 10.25743/ICT.2019.24.6.015.
2. Kleshchenko A.D., Virchenko O.V., Savitskaya O.V. Sputnikovyy monitoring sostoyaniya i
produktivnosti posevov zernovykh kul'tur [Satellite monitoring of the state and productivity of
grain crops], Tr. VNIISKhM [Proceedings of VNIISHM], 2013, Issue 38, pp. 54-70.
3. Sharpley A.N.; Williams J.R. The Erosion-Productivity Impact Calculator (EPIC). Technical
bulletin (United States. Department of Agriculture), No. 1768, 235 p.
4. Sladkikh L.A., Saprykin E.I., Zakhvatov M.G., Sakharova E.Yu. Tekhnologiya monitoringa
sostoyaniya posevov po dannym distantsionnogo zondirovaniya Zemli na yuge Zapadnoy
Sibiri [Technology for monitoring the state of crops according to remote sensing of the Earth
in the south of Western Siberia], GEOMATICS [GEOMATICS], 2016, No. 2, pp. 39-48.
5. Iizumia T., Shinb Y., Kima W., Kimb M., Choib J. Global crop yield forecasting using seasonal
climate information from a multi-model ensemble, Climate Services., 2018, Vol. 11, pp. 13-23.
– https://doi.org/10.1016/j.cliser.2018.06.003.
6. Shashank V., Thomas K., Sangram V., Auroop G., Ganguly R. Data science for weather impacts
on crop yield, Frontiers in Sustainable Food Systems, 2020. Available at:
https://doi.org/10.3389/fsufs.2020.00052.
7. Bulletins and Publications. European Commission Joint Research Center. Available at:
http://mars.jrc.ec.europa.eu/mars/ Bulletins-.Publications.
8. Zagorovskaya V. Sam sebe meteorolog. Pogoda s poley: zachem khozyaystva obzavodyatsya
meteostantsiyami [Himself a meteorologist. Weather from the fields: why farms acquire
weather stations], Agrotekhnika i tekhnologii [Agrotechnics and technologies], 2020, No. 5.
Available at: https://www.agroinvestor.ru/agrotechnika/81/.
9. Pogoda v pole [Weather in the field]. Available at: https://pogodavpole.rf.
10. 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
Kabardino-Balkarskogo nauchnogo tsentra RAN [Proceedings of the Kabardino-Balkarian
Scientific Center of the Russian Academy of Sciences], 2021, No. 6 (104). pp. 155-165. DOI:
https://doi.org/10.35330/1991-6639-2021-6-104-155-165.
11. Bzhikhatlov K.Ch., Unagasov A.A. Programma dlya podklyucheniya besprovodnykh
meteostantsiy k intellektual'noy sisteme prinyatiya resheniy [Program for connecting wireless
weather stations to an intelligent decision-making system], Certificate of registration of the
computer program No. 2021665983, 06.10.2021.
12. Nagoev Z.V. Intellektika, ili myshlenie v zhivykh i iskusstvennykh sistemakh [Intelligence, or
thinking in living and artificial systems]. Nal'chik: Izd-vo KBNTS RAN, 2013, 213 p.
13. Nagoev Z.V., Bzhikhatlov K.Ch., Pshenokova I.A., Nagoeva O.V., Sundukov Z.A., Atalikov B.A.,
Chechenova N.A., Malyshev D.A. Avtonomnyy sintez prostranstvennykh ontologiy v sisteme
prinyatiya resheniy mobil'nogo robota na osnove samoorganizatsii mul'tiagentnoy
neyrokognitivnoy arkhitektury [Autono-mous synthesis of spatial ontologies in the decisionmaking
system of a mobile robot based on self-organization of multi-agent neurocognitive architecture],
Izvestiya Kabardino-Balkarskogo nauchnogo tsentra RAN [Bulletin of the
Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences], 2020, No. 6 (98),
pp. 68-79. DOI: https://doi.org/10.35330/1991-6639-2020-6-98-68-79.
14. Nagoev Z.V. Multiagent recursive cognitive architecture, Biologically Inspired Cognitive Architectures
2012: Proceedings of the third annual meeting of the BICA Society, in Advances in
Intelligent Systems and Computing series. Springer, 2012, pp. 247-248.
15. 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.
16. Nagoev Z., Nagoeva O., Pshenokova I., Bzhikhatlov K., Gurtueva I., Kankulov S. Multiagent
neurocognitive models of the processes of understanding the natural language description of
the mission of autonomous robots, Biologically Inspired Cognitive Architectures 2021: Proceedings
of the 12th Annual Meeting of the BICA Society. Studies in Computational Intelligence,
volume XVI, 625 Cham, Switzerland: Springer Nature, 2022. ISSN: 1860-949X.
17. Gurtueva I., Nagoeva O. and Pshenokova I. Speech recognition algorithm for natural language
management systems under variety of accents, E3S Web of Conferences, 2020, Vol. 164,
10015. DOI: https://doi.org/10.1051/e3sconf/202016410015.
18. Pshenokova I.A., Sundukov Z.A. Razrabotka imitatsionnoy modeli stsenarnogo
prognozirovaniya povedeniya intellektual'nogo agenta na osnove invarianta rekursivnoy
mul'tiagentnoy neyrokognitivnoy arkhitektury [Development of a simulation model for scenario
prediction of the behavior of an intelligent agent based on the invariant of a recursive multiagent
neurocognitive architecture], Izvestiya Kabardino-Balkarskogo nauchnogo tsentra RAN
[News of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences],
2020, No. 6 (98), pp. 80-90. DOI: https://doi.org/10.35330/1991-6639-2020-6-98-80-90.
19. Nagoev Z., Pshenokova I., Anchekov M. Model of the reasoning process in a multiagent cognitive
system, Procedia Computer Science, 2020, Vol. 169, pp. 615-619. Available at:
https://doi.org/10.1016/j.procs.2020.02.202.
20. Nagoev Z., Pshenokova I., Nagoeva O., and Kankulov S. Situational analysis model in an intelligent
system based on multi-agent neurocognitive architectures, Journal of Physics: Conference
Series (JPCS) 2131 (2021) 022103. DOI:10.1088/1742-6596/2131/2/022103.
distantsionnogo zondirovaniya Zemli (na primere soi) [Forecasting crop yields based on Earth remote
sensing data (on the example of soybeans], Vychislitel'nye tekhnologii [Computational technologies],
2019, Vol. 24, No. 6, pp. 125-133. DOI: 10.25743/ICT.2019.24.6.015.
2. Kleshchenko A.D., Virchenko O.V., Savitskaya O.V. Sputnikovyy monitoring sostoyaniya i
produktivnosti posevov zernovykh kul'tur [Satellite monitoring of the state and productivity of
grain crops], Tr. VNIISKhM [Proceedings of VNIISHM], 2013, Issue 38, pp. 54-70.
3. Sharpley A.N.; Williams J.R. The Erosion-Productivity Impact Calculator (EPIC). Technical
bulletin (United States. Department of Agriculture), No. 1768, 235 p.
4. Sladkikh L.A., Saprykin E.I., Zakhvatov M.G., Sakharova E.Yu. Tekhnologiya monitoringa
sostoyaniya posevov po dannym distantsionnogo zondirovaniya Zemli na yuge Zapadnoy
Sibiri [Technology for monitoring the state of crops according to remote sensing of the Earth
in the south of Western Siberia], GEOMATICS [GEOMATICS], 2016, No. 2, pp. 39-48.
5. Iizumia T., Shinb Y., Kima W., Kimb M., Choib J. Global crop yield forecasting using seasonal
climate information from a multi-model ensemble, Climate Services., 2018, Vol. 11, pp. 13-23.
– https://doi.org/10.1016/j.cliser.2018.06.003.
6. Shashank V., Thomas K., Sangram V., Auroop G., Ganguly R. Data science for weather impacts
on crop yield, Frontiers in Sustainable Food Systems, 2020. Available at:
https://doi.org/10.3389/fsufs.2020.00052.
7. Bulletins and Publications. European Commission Joint Research Center. Available at:
http://mars.jrc.ec.europa.eu/mars/ Bulletins-.Publications.
8. Zagorovskaya V. Sam sebe meteorolog. Pogoda s poley: zachem khozyaystva obzavodyatsya
meteostantsiyami [Himself a meteorologist. Weather from the fields: why farms acquire
weather stations], Agrotekhnika i tekhnologii [Agrotechnics and technologies], 2020, No. 5.
Available at: https://www.agroinvestor.ru/agrotechnika/81/.
9. Pogoda v pole [Weather in the field]. Available at: https://pogodavpole.rf.
10. 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
Kabardino-Balkarskogo nauchnogo tsentra RAN [Proceedings of the Kabardino-Balkarian
Scientific Center of the Russian Academy of Sciences], 2021, No. 6 (104). pp. 155-165. DOI:
https://doi.org/10.35330/1991-6639-2021-6-104-155-165.
11. Bzhikhatlov K.Ch., Unagasov A.A. Programma dlya podklyucheniya besprovodnykh
meteostantsiy k intellektual'noy sisteme prinyatiya resheniy [Program for connecting wireless
weather stations to an intelligent decision-making system], Certificate of registration of the
computer program No. 2021665983, 06.10.2021.
12. Nagoev Z.V. Intellektika, ili myshlenie v zhivykh i iskusstvennykh sistemakh [Intelligence, or
thinking in living and artificial systems]. Nal'chik: Izd-vo KBNTS RAN, 2013, 213 p.
13. Nagoev Z.V., Bzhikhatlov K.Ch., Pshenokova I.A., Nagoeva O.V., Sundukov Z.A., Atalikov B.A.,
Chechenova N.A., Malyshev D.A. Avtonomnyy sintez prostranstvennykh ontologiy v sisteme
prinyatiya resheniy mobil'nogo robota na osnove samoorganizatsii mul'tiagentnoy
neyrokognitivnoy arkhitektury [Autono-mous synthesis of spatial ontologies in the decisionmaking
system of a mobile robot based on self-organization of multi-agent neurocognitive architecture],
Izvestiya Kabardino-Balkarskogo nauchnogo tsentra RAN [Bulletin of the
Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences], 2020, No. 6 (98),
pp. 68-79. DOI: https://doi.org/10.35330/1991-6639-2020-6-98-68-79.
14. Nagoev Z.V. Multiagent recursive cognitive architecture, Biologically Inspired Cognitive Architectures
2012: Proceedings of the third annual meeting of the BICA Society, in Advances in
Intelligent Systems and Computing series. Springer, 2012, pp. 247-248.
15. 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.
16. Nagoev Z., Nagoeva O., Pshenokova I., Bzhikhatlov K., Gurtueva I., Kankulov S. Multiagent
neurocognitive models of the processes of understanding the natural language description of
the mission of autonomous robots, Biologically Inspired Cognitive Architectures 2021: Proceedings
of the 12th Annual Meeting of the BICA Society. Studies in Computational Intelligence,
volume XVI, 625 Cham, Switzerland: Springer Nature, 2022. ISSN: 1860-949X.
17. Gurtueva I., Nagoeva O. and Pshenokova I. Speech recognition algorithm for natural language
management systems under variety of accents, E3S Web of Conferences, 2020, Vol. 164,
10015. DOI: https://doi.org/10.1051/e3sconf/202016410015.
18. Pshenokova I.A., Sundukov Z.A. Razrabotka imitatsionnoy modeli stsenarnogo
prognozirovaniya povedeniya intellektual'nogo agenta na osnove invarianta rekursivnoy
mul'tiagentnoy neyrokognitivnoy arkhitektury [Development of a simulation model for scenario
prediction of the behavior of an intelligent agent based on the invariant of a recursive multiagent
neurocognitive architecture], Izvestiya Kabardino-Balkarskogo nauchnogo tsentra RAN
[News of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences],
2020, No. 6 (98), pp. 80-90. DOI: https://doi.org/10.35330/1991-6639-2020-6-98-80-90.
19. Nagoev Z., Pshenokova I., Anchekov M. Model of the reasoning process in a multiagent cognitive
system, Procedia Computer Science, 2020, Vol. 169, pp. 615-619. Available at:
https://doi.org/10.1016/j.procs.2020.02.202.
20. Nagoev Z., Pshenokova I., Nagoeva O., and Kankulov S. Situational analysis model in an intelligent
system based on multi-agent neurocognitive architectures, Journal of Physics: Conference
Series (JPCS) 2131 (2021) 022103. DOI:10.1088/1742-6596/2131/2/022103.
Published
2022-04-21
Issue
Section
SECTION I. PROSPECTS FOR THE USE OF ROBOTIC SYSTEMS
