Improvement of cereal harvest programming methods using computer simulation information technology

Authors

  • Oleksandr M. Terentiev Doctor of Technical Sciences, Associate Professor, Principal researcher, Institute of Telecommunications and Global Information Space of the National Academy of Sciences of Ukraine, Kyiv, Ukraine https://orcid.org/0000-0002-4288-1753
  • Denys I. Prosyankin Graduate student, Institute of Telecommunications and Global Information Space of the National Academy of Sciences of Ukraine, Kyiv, Ukraine https://orcid.org/0009-0000-4402-6921

DOI:

https://doi.org/10.32347/2411-4049.2023.4.152-169

Keywords:

modelling, mathematical models, Bayesian network, biological processes, ecological crop production

Abstract

The paper is dedicated to a topical scientific and applied problem – the development of information technology of computer modelling intended for programming the yield of agricultural crops. The paper describes information technology of computer modelling of the yield of agricultural crops (on the example of Avena sativa subsp. nudisativa), which is based on the application of Bayesian methods to modelling and prediction in conditions of statistical, parametric and structural uncertainty. The study was based on the materials of laboratory experiments carried out in conditions close to natural, on the prediction of physiological processes occurring in plants under the influence of regulated and unregulated factors. Proposed approach described the change in the productivity of grain crops, in particular Avena sativa subsp. nudisativa, depending on the parameters of plant growth and development, photosynthetic apparatus and duration of its functioning. Scientific novelty of the work was application of probabilistic and statistical models in the form of Bayesian networks in the system of programming the yield of agricultural crops. The paper considered several scenarios of the combined effect of growth regulators and herbicides on the productivity of Avena sativa subsp. nudisativa. Net productivity of photosynthesis was chosen as the target variable of the studied process. Mathematical models in the form of Bayesian network turned out to be adequate for the process chosen for modelling. Achieved error of model classification was about 20%. The model structure was built in Genie 2.0 modelling system. It was found that by researching and simulating potential opportunities of ecological features of plants, it was possible to achieve an increase in yield by reducing the doses of herbicides and growth regulators by their combined use, which significantly increased the crop quality. Proposed information technology uses methods of intelligent data analysis, has a modular structure and can be used separately and as part of other information and analytical systems.

References

Bidjuk, P.I., Terent'jev, O.M., Prosjankina-Zharova, T.I., & Efendijev, V.V. (2017). Prognozne modeljuvannja nelinijnyh nestacionarnyh procesiv u roslynnyctvi z vykorystannjam instrumentiv SAS Enterprise Miner. Naukovi visti NTUU KPI, 1, 24-36 [in Ukrainian]. https://doi.org/10.20535/1810-0546.2017.1.87423

Westra, E.P., Shaner, D.L., Westra, P.H., & Chapman, P.L. (2014). Dissipation and Leaching of Pyroxasulfone and S-Metolachlor. Weed Technology, 28(1), 72–81. https://doi.org/10.1614/WT-D-13-00047.1

Gulner, G., Kömives, T., & Rennenberg, H. (2001). Enchanced tolerance of transgenic poplar plants overexpressing γ-glutamylcysteine synthetase towards chloroacetanilide herbicides. The Journal of Experimental Botany, 52(358), 971–979. https://doi.org/10.1093/jexbot/52.358.971

Foyer, C.H., & Noctor, G. (2009). Redox regulation in photosynthetic organisms: Signaling, acclimation and practical implications. Antioxidants and Redox Signaling, 11, 862–905. https://doi.org/10.1089/ars.2008.2177

Zabolotna, A.V., Zabolotnyj, O.I., Rozbors'ka, L.V., Zhyljak, I.D., & Dacenko A.A. (2021). Vmist pigmentiv i chysta produktyvnist' fotosyntezu kukurudzy za vykorystannja reguljatoriv rostu roslyn. Visnyk Sums'kogo nacional'nogo agrarnogo universytetu Serija «Agronomija i biologija», 4(46), 9-15 [in Ukrainian]. https://doi.org/10.32845/agrobio.2021.4.2

Wang, G., Zhuang, L., Mo, L., Yi, X., Wu, P., & Wu, X. (2023). BAG: A Linear-Nonlinear Hybrid Time Series Prediction Model for Soil Moisture. Agriculture, 13(2), 379. https://doi.org/10.3390/agriculture13020379

Ma, D., Maki, H., Neeno, S., Zhang, L., Wang, L., & Jin, J. (2020). Application of non-linear partial least squares analysis on prediction of biomass of maize plants using hyperspectral images. Biosystems Engineering, 200, 40-54. https://doi.org/10.1016/j.biosystemseng.2020.09.002

Paine, C. E. T., Marthews, T. R., Vogt, D. R., Purves, D., Rees, M., Hector, A., & Turnbull, L. A. (2012). How to fit nonlinear plant growth models and calculate growth rates: an update for ecologists. Methods in Ecology and Evolution, 3, 245–256. https://doi.org/10.1111/j.2041-210X.2011.00155.x

KPMG. Retrieved from https://kpmg.com/ua/uk/home.html

EOS Data Analytics. Retrieved from https://eos.com/uk/

Informacijna tehnologija monitoryngu vyroshhuvannja ozymyh kul'tur (Programuvannja vrozhaju ozymyh sil's'kogospodars'kyh kul'tur). Retrieved from https://scp.knu.ua/ua/kataloh-innovatsiinykh-proektiv-3/361-informatsiina-tekhnolohiia-monitorynhu-vyroshchuvannia-ozymykh-kultur-prohramuvannia-vrozhaiu-ozymykh-silskohospodarskykh-kultur [in Ukrainian].

Proekt AgroTehintelekt. Retrieved from https://www.ndipvt.com.ua/agrotehintel.html [in Ukrainian].

Zgurovs'kyj, M.Z., Bidjuk, P.I., Terent'jev, O.M., & Prosjankina-Zharova T.I. (2015). Bajjesivs'ki merezhi u systemah pidtrymky pryjnjattja rishen'. Kyi'v: TOV «Vydavnyche Pidpryjemstvo «Edel'vejs» [in Ukrainian].

de Carvalho, P., Nicolai, M., Rodrigues, Ferreira, R. et al. (2009). Herbicide selectivity by differential metabolism: considerations for reducing crop damages sci. agric. (piracicaba, braz.). Scientia Agricola, 66(1), 136-142. https://doi.org/10.1590/S0103-90162009000100020

Loboda, O.M., & Hudik, N.D. (2021). Vykorystannja ekspertnyh ocinok dlja vyznachennja priorytetnyh naprjamiv vprovadzhennja cyfrovyh tehnologij v agrobiznesi. Agrosvit, 4, 38-44 [in Ukrainian]. https://doi.org/10.32702/2306&6792.2021.4.38

Kokovihin, S.V., & Kovalenko, V.P. (2019). Matematychne modeljuvannja rivniv produktyvnosti bagatorichnyh bobovyh kul'tur v umovah Lisostepu Ukrai'ny. Tavrijs'kyj naukovyj visnyk, 108, 39-45 [in Ukrainian]. https://doi.org/10.32851/2226-0099.2019.108.6

Bondarenko, L.V. (2017). Vprovadzhennja novyh informacijnyh tehnologij u profesijnu dijal'nist' agronoma. Naukova pracja. Pedagogika, 281 (293), 47-53 [in Ukrainian].

Pearl, J. (2000). Causality: models, reasoning, and inference. Cambridge University Press.

Jensen, F.V. (2001). Bayesian networks and decision graphs. New York: Springer. https://doi.org/10.1007/978-1-4757-3502-4

Spiegelhalter, D., Dawid, P., Lauritzen, S., & Cowell, R. (1993). Bayesian analysis in expert systems. Statistical Science, 8 (3), 219–247.

Trofymchuk, O., Bidiuk, P., Terentiev, O., & Prosyankina-Zharova, T. (2019). Decision Support Systems for Modelling, Forecasting and Risk Estimation. Riga: LAP Lambert Academic Publishing.

Lauritzen, S.L., & Spiegelhalter, D.J. (1988). Local computations with probabilities on graphical structures and their application to expert systems. Journal Royal Statistics Society, series B (Methodology). 50 (2), 157-194.

Karpenko, V.P., & Prosjankin, D.I. (2015). Lipoperoksydacijni ta antyoksydantni procesy v roslynah vivsa golozernogo za dii' biologichno aktyvnyh rechovyn. Visnyk Umans'kogo Nacional'nogo universytetu sadivnyctva, 1, 47-50 [in Ukrainian].

Grycajenko, Z.M. Grycajenko, A.O., & Karpenko, V.P. (2003). Metody biologichnyh ta agrohimichnyh doslidzhen' roslyn i g'runtiv. Kyi'v: ZAT «Nichlava» [in Ukrainian].

Pateiro, M., Domínguez, R., Munekata, P.E.S., Nieto, G., Bangar, S.P., Dhama, K., & Lorenzo, J.M. (2023). Bioactive Compounds from Leaf Vegetables as Preservatives. Foods., 12 (3), 637. https://doi.org/10.3390/foods12030637

Vyznachennja rozchynnyh suhyh rechovyn refraktometrychnym metodom (ISO 2173:2003 IDT): DSTU ISO 2173:2007 (2007). Retrieved from http://csm.kiev.ua/nd/nd.php?b=1&l=24791 [in Ukrainian].

Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, Prediction and Search. Part of the book series: Lecture Notes in Statistics (LNS, vol. 81). Berlin: Springer Verlag. https://doi.org/10.1007/978-1-4612-2748-9

Genie 2.0. Retrieved from https://www.bayesfusion.com/genie/

Kelangath, S., Das, P.K., Quigley, J., & Hirdaris, S.E. (2012). Risk analysis of damaged ships - A data-driven Bayesian approach. Ships and Offshore Structures. 7 (3), 333-347. https://doi.org/10.1080/17445302.2011.592358

Spirtes, P., Glymour C., & Scheines, R. (1991). From probability to causality. Philosophical Studies, 64, 1–36. https://doi.org/10.1007/BF00356088

Published

2023-12-26

How to Cite

Terentiev, O. M., & Prosyankin, D. I. (2023). Improvement of cereal harvest programming methods using computer simulation information technology. Environmental Safety and Natural Resources, 48(4), 152–169. https://doi.org/10.32347/2411-4049.2023.4.152-169

Issue

Section

Information technology and mathematical modeling