: pp. 5-10
Lviv Polytechnic National University
Lviv Polytechnic National University
Lviv Polytechnic National University

The increasing demand for precision agriculture has prompted the integration of advanced technologies to optimize agricultural practices. This article presents an approach to agricultural field data processing using a cloud-based data pipeline. The system leverages data from various sensors deployed in the fields to collect real-time information on key parameters such as soil moisture, temperature, humidity, etc. The collected data is transmitted to the cloud where it undergoes a series of data processing and analysis stages. The article demonstrates the effectiveness of the cloud-based data pipeline in enhancing agricultural resilience. It facilitates prompt decision-making by farmers and stakeholders based on real-time data analysis. Additionally, the system offers a valuable tool for monitoring and optimizing irrigation strategies, resource allocation, and crop management practices. This research highlights the potential of cloud-based data pipelines in revolutionizing precision agriculture. The ability to measure and analyze agricultural field data accurately and efficiently opens new avenues for sustainable farming practices and mitigating risks related to wildfires and droughts.

[1] E. Fukase, W. Martin, “Economic growth, convergence, and world food demand and supply”, World Development, Volume 132, 2020. DOI: j.worlddev.2020.104954

[2] S. P. Poznyak, “Black soils of Ukraine: geography, genesis and current state”, 2016. DOI: ugz2016.01.009

[3] M. S. Alkatheiri, “Artificial intelligence assisted improved human-computer interactions for computer systems:”, Computers and Electrical Engineering, Volume 101, 2022. DOI:

[4] H. E. Pence, “What is Big Data and Why is it Important?”, Journal of Educational Technology Systems, 43(2), 159–171, 2014. DOI:

[5] M. B. Hoy (2015), “The ‘Internet of Things’: What It Is and What It Means for Libraries”, Medical Reference Services Quarterly, 34:3, 353-358. DOI: 10.1080/02763869.2015.1052699

[6] N. Biswas, "A new approach for conceptual extractiontransformation-loading process modeling,”, International Journal of Ambient Computing and Intelligence 10.1, 30-45, 2019. DOI: DOI: 10.4018/IJACI.2019010102

[7] A. Gupta, P. Goswami, N. Chaudhary, R. Bansal, "Deploying an Application using Google Cloud Platform," 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 2020, pp. 236-239. DOI:

[8] G. van Dongen, D. van den Poel, "Evaluation of Stream Processing Frameworks," IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 8, pp. 1845-1858, 2020. DOI:

[9] B. Grados, H. Bedon. “Software Components of an IoT Monitoring Platform in Google Cloud Platform: A Descriptive Research and an Architectural Proposal”, Communications in Computer and Information Science, vol 1193. 2019. DOI:

[10] Z. Dobesova, "Programming language Python for data processing," 2011 International Conference on Electrical and Control Engineering, 2011, pp. 4866-4869, DOI: 10.1109/ICECENG.2011.6057428

[11] J. Shah and D. Dubaria, "Building Modern Clouds: Using Docker, Kubernetes & Google Cloud Platform," 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, 2019, pp. 0184-0189, DOI: 10.1109/ CCWC.2019.8666479

[12] F. Bergsma, B. Dowling, F. Kohlar, J. Schwenk, D. Stebila, “Multi-Ciphersuite Security of the Secure Shell (SSH) Protocol”, 2014 ACM SIGSAC Conference on Computer and Communications Security, 369–381, 2014. DOI

[13] J. Gascon-Samson, F.-P. Garcia, B. Kemme, J. Kienzle, "Dynamoth: A Scalable Pub/Sub Middleware for LatencyConstrained Applications in the Cloud," 2015 IEEE 35th International Conference on Distributed Computing Systems, 2015, pp. 486-496, DOI: ICDCS.2015.56

[14] J. Sreemathy, R. Brindha, M. Selva Nagalakshmi, N. Suvekha, N. Karthick Ragul and M. Praveennandha, "Overview of ETL Tools and Talend-Data Integration," 2021 7th International Conference on Advanced Computing and Communication Systems, 2021, pp. 1650-1654, DOI:

[15] D. Dzulhikam, M. E. Rana, "A Critical Review of Cloud Computing Environment for Big Data Analytics," 2022 International Conference on Decision Aid Sciences and Applications, 2022, pp. 76-81, DOI: DASA54658.2022.9765168

[16] S. M. Ali, N. Gupta, G. K. Nayak, R. K. Lenka, "Big data visualization: Tools and challenges," 2016 2nd International Conference on Contemporary Computing and Informatics, 2016, pp. 656-660, DOI: IC3I.2016.7918044

[17] B. Xia, P. Gong, “Review of business intelligence through data analysis”, Benchmarking: An International Journal. 21. 300-311, 2014. DOI: 0050.

[18] M. S. Gounder, V. V. Iyer, A. Al Mazyad, "A survey on business intelligence tools for university dashboard development," 2016 3rd MEC International Conference on Big Data and Smart City, 2016, pp. 1-7, DOI: ICBDSC.2016.7460347