MATHEMATICAL, SOFTWARE AND HARDWARE SUPPORT OF THE CONCEPTUAL MODEL OF THE INFORMATION SYSTEM OF PRECISION AGRICULTURE

Authors

DOI:

https://doi.org/10.37943/15TKFW1223

Keywords:

precision farming, universal programmable controller, mathematical support, hardware, software, meteorological control, forecasting, yield

Abstract

This study analyzes the current situation of application of precision farming technologies and solutions by agricultural enterprises of the Republic of Kazakhstan. The main players and used solutions have been identified. The statistics of application, as well as the potential of use is examined. Within the framework of the analysis of the applied solutions the advantages and disadvantages of competitors in the market were determined. It was defined that the applied systems provide the possibility of remote management, but EGISTIC is more focused on the management of all processes of the farm, including the warehouse, while John Deere is focused on the management and analytics of agricultural machinery. EGISTIC offers features for warehousing and inventory planning, something not found in the base version of John Deere Operations Center. John Deere focuses on data sharing which can be important for large farms or groups of farmers. EGISTIC makes extensive use of satellite imagery to analyze field conditions which can be a great asset for identifying problem areas and planning interventions. Depending on the specific needs and priorities of an agribusiness, one system may be preferable to another. If machinery management is the main focus, John Deere might be the best choice. If in-depth analysis of field conditions and inventory control is important, EGISTIC may be more appropriate. By analyzing, the directions for research are highlighted. A conceptual model of information system for precision farming is developed. Hardware for realization of the conceptual model is possible on the basis of universal programmable logic controller of modular architecture being developed. Within the limits of the given research the conceptual model of the universal programmable logic controller of modular architecture and the structural model of the software of the universal programmable logic controller of modular architecture have been developed. The interaction with the conceptual adaptive model of information and communication system is also considered. This paper analyzes the key principles and functions of both the universal programmable logic controller and the information and communication system, as well as their possible integration within a single concept.

References

Di Paola, A., Valentini, R., & Santini, M. (2016). Na peremeshchenii dostupnykh stroitel'nykh statej i proizvodnykh modelej dlya issledovanij i issledovanij v sel'skom khozyajstve [On the movement of available construction articles and derived models for research and research in agriculture]. Journal of Science of Food and Agriculture, 96(3), 709-714.

Li, S., Xu, L., Jing, Y., Yin, H., Li, X., & Guan, X. (2021). High-quality vegetation index production generation: A review of NDVI time series reconstruction techniques. International Journal of Applied Earth Observation and Geoinformation, 105, 102640.

Pashkov, S., & Mazhitova, G. (2023). Digitalization of agriculture in Kazakhstan: Regional experience. Geographical Bulletin, 4(59).

Žuraulis, V., & Pečeliūnas, R. (2023). The Architecture of an Agricultural Data Aggregation and Conversion Model for Smart Farming. Appl. Sci.,13, 11216. https://doi.org/10.3390/app132011216

Alahmad, T., Neményi, M., & Nyéki, A. (2023). Applying IoT Sensors and Big Data to Improve Precision Crop Production: A Review. Agronomy, 13, 2603. https://doi.org/10.3390/agronomy13102603

Bin, L., Shahzad, M., Khan, H., Bashir, M., Ullah, A., & Siddique, M. (2023). Sustainable Smart Agriculture Farming for Cotton Crop: A Fuzzy Logic Rule Based Methodology. Sustainability,15, 13874. https://doi.org/10.3390/su151813874

Song, S., Yang, R., Cui, X., & Chen, Q. (2023). County-Scale Spatial Distribution of Soil Nutrients and Driving Factors in Semiarid Loess Plateau Farmland, China. Agronomy, 13, 2589. https://doi.org/10.3390/agronomy13102589

Knezevic, I., Blay-Palmer, A., & Clause, C. (2023). Recalibrating Data on Farm Productivity: Why We Need Small Farms for Food Security. Sustainability, 15, 14479. https://doi.org/10.3390/su151914479

Butt, R., Rehman, T., & Qureshi, M. (2023). A Smart IoT-Enabled Cage for the Farming of Ground Birds. Eng. Proc., 46, 26. https://doi.org/10.3390/engproc2023046026

Maksimal'naya vygoda i minimal'nye riski: kak umnoe sel'skoe khozyajstvo oblegchaet zhizn' agrariyam [Maximum benefit and minimum risks: how smart agriculture makes life easier for farmers]. (2023, October 10). Agribusiness.Kazakhstan. https://agbz.kz/maksimalnaya-vygoda-i-minimalnye-riski-kak-umnoe-selskoe-hozyajstvo-oblegchaet-zhizn-agrariyam/

Tuleeva, K. (2021). Cifrovye tekhnologii v polevodstve Kazakhstana [Digital technologies in the field production of Kazakhstan]. Crops & oilseeds. Kazakhstan. https://margin.kz/news/9523/tsifrovye-tehnologii-v-polevodstve-kazahstana/

Ministry of Digital Development, Innovation and Aerospace Industry of the Republic of Kazakhstan. (2017). Report on the implementation of the State program "Digital Kazakhstan" for 2018-2022 ICRIAP RK.

State Register of rights to objects. (2018). National Institute of Intellectual Property. https://copyright.kazpatent.kz/?!.iD=wQEy

Singh, A., & Sharma, V. (2023). Commissionable -2 bifurcation в discrete predator–prey system with constant yield predator harvesting. International Journal of Biomathematics, 16(05), 2250109.

Horie, T., Yajima, M., & Nakagawa, H. (1992). Yield forecasting. Agricultural systems, 40(1-3), 211-236.

Basso, B., & Liu, L. (2019). Seasonal crop yield forecast: Methods, applications, and accuracies. Advances in agronomy, 154, 201-255.

Paudel, D., Boogaard, H., de Wit, A., van der Velde, M., Claverie, M., Nisini, L., ... & Athanasiadis, I. (2022). Machine learning for regional crop yield forecasting in Europe. Field Crops Research, 276, 108377.

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Published

2023-09-30

How to Cite

Neftissov, A. ., Biloshchytskyi, A., Toxanov, S. ., Ordabayev, S. ., Kuchansky, O. ., Andrashko, Y. ., & Vatskel, V. . . (2023). MATHEMATICAL, SOFTWARE AND HARDWARE SUPPORT OF THE CONCEPTUAL MODEL OF THE INFORMATION SYSTEM OF PRECISION AGRICULTURE. Scientific Journal of Astana IT University, 15(15), 55–70. https://doi.org/10.37943/15TKFW1223

Issue

Section

Information Technologies
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