WEIGHTS-BASED REAL-TIME ROUTING PLATFORM FOR DIGITAL AGRICULTURE IN UKRAINE

Volodymyr Nazarenko

doi:10.36074/grail-of-science.17.10.2025.018

WEIGHTS-BASED REAL-TIME ROUTING PLATFORM FOR DIGITAL AGRICULTURE IN UKRAINE

Authors

Volodymyr Nazarenko The National University of Life and Environmental Sciences of Ukraine, Ukraine https://orcid.org/0000-0002-7433-2484

DOI:

https://doi.org/10.36074/grail-of-science.17.10.2025.018

Keywords:

digital agriculture, real-time routing, weighted decision model, sustainability, IoT; UAV, middleware architecture, economic optimization, Ukraine, SDGs

Summary

This study develops and evaluates a weights-based, real-time routing platform designed for digital agriculture systems in Ukraine. Building on previous research into UAV–AI–IoT integration and sustainability measurement architectures, the proposed platform introduces a multi-criteria decision engine that optimizes agricultural logistics under variable economic, environmental, and infrastructure conditions.

The routing module integrates multi-source data streams - IoT telemetry, administrative-economic datasets, satellite imagery, and real-time cost indicators (UAH/km) - through an interoperable middleware following FAIR and OGC standards. The system dynamically adjusts weights (time, cost, quality, risk, and sustainability) to reflect fuel price volatility, infrastructure degradation, and wartime mobility restrictions.

A Kyiv metropolitan agricultural belt case study demonstrates 8–15% cost reductions, improved on-time task completion, and enhanced data continuity during UAV flight restrictions. The platform contributes to implementing the EU Green Deal, CAP, and SDG 9/11/12/17 by aligning economic efficiency with sustainability reporting.

The article proposes a conceptual and computational model that can be a policy and operational tool for optimizing agricultural logistics and strengthening resilience in Ukraine’s agri-digital transformation.

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References

Agrawal, J., & Arafat, M. Y. (2024). Transforming farming: A review of AI-powered UAV technologies in precision agriculture. Drones, 8(11), 664. https://doi.org/10.3390/drones8110664 DOI: https://doi.org/10.3390/drones8110664

Abdelmoneim, A. A., Kimaita, H. N., Al Kalaany, C. M., Derardja, B., Dragonetti, G., & Khadra, R. (2025). IoT sensing for advanced irrigation management: A systematic review of trends, challenges, and future prospects. Sensors (Basel, Switzerland), 25(7), 2291. doi: 10.3390/s25072291. DOI: https://doi.org/10.3390/s25072291

Awais, M., Wang, X., Hussain, S., Aziz, F., & Mahmood, M. Q. (2025). Advancing precision agriculture through digital twins and smart farming technologies: A review. AgriEngineering, 7(5), 137. https://doi.org/10.3390/agriengineering7050137. DOI: https://doi.org/10.3390/agriengineering7050137

European Commission. (2020). A Farm to Fork Strategy for a fair, healthy, environmentally-friendly food system. https://food.ec.europa.eu/horizontal-topics/farm-fork-strategy_en.

United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development. https://sdgs.un.org/2030agenda.

Urdu, D., Berre, A. J., Sundmaeker, H., Rilling, S., Roussaki, I., Marguglio, A., … Wolfert, S. (2024). Aligning interoperability architectures for digital agri-food platforms. Computers and Electronics in Agriculture, 224, 109194. https://doi.org/10.1016/j.compag.2024.109194. DOI: https://doi.org/10.1016/j.compag.2024.109194

Obayi, R., Choudhary, S., Nayak, R., & Ramanjaneyulu, G. V. (2024). Pragmatic interoperability for human–machine value creation in agri-food supply chains. Information Systems Frontiers. Advance online publication. https://doi.org/10.1007/s10796-024-10567-x. DOI: https://doi.org/10.1007/s10796-024-10567-x

Open Geospatial Consortium. (2022). OGC SensorThings API Part 1: Sensing (v1.1). https://docs.ogc.org/is/18-088/18-088.html.

Deshmukh, R. A., Collarana, D., Gelhaar, J., Theissen-Lipp, J., Lange, C., Arnold, B. T., ... & Decker, S. (2024). Challenges and opportunities for enabling the next generation of cross-domain dataspaces. SDS@ ESWC.

Nazarenko, V. (2023). Urban futures reimagined: Advanced

ecological-economic frameworks for sustainable growth [Monograph]. National University of Life and Environmental Sciences of Ukraine. https://dglib.nubip.edu.ua/handle/123456789/10692.

Nazarenko, V., & Ostroushko, B. (2024). Smart city management system utilizing micro-services and IoT-based systems. Energiya, 1(71), 29–38. https://doi.org/10.31548/energiya1(71).2024.029. DOI: https://doi.org/10.31548/energiya1(71).2024.029

Nazarenko, V., & Martyn, A. (2025). Urban growth and agrarian dynamics: Evaluating the Kyiv agglomeration’s economic landscape. Economics and Business Management, 2(16), 24–41. https://doi.org/10.31548/economics/2.2025.24. DOI: https://doi.org/10.31548/economics/2.2025.24

Zhai, Z., Martínez, J. F., Beltran, V., & Martínez, N. L. (2020). Decision support systems for Agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 105256. https://doi.org/10.1016/j.compag.2020.105256. DOI: https://doi.org/10.1016/j.compag.2020.105256

Miller, T., Mikiciuk, G., Durlik, I., Mikiciuk, M., Łobodzińska, A., & Śnieg, M. (2025). The IoT and AI in agriculture: The time is now-A systematic review of smart sensing technologies. Sensors, 25(12), 3583. https://doi.org/10.3390/s25123583. DOI: https://doi.org/10.3390/s25123583

Europa Technologies. (n.d.). Global 1000 Atlas: Kyiv metropolitan region (Map ID 48128). Retrieved October 9, 2025, from https://www.europa.uk.com/global-1000-atlas/map/?pid=48128.