Measurement and analysis of agricultural field state using cloud-based data processing pipeline Preventing potential robbery crimes using deep learning algorithm of data processing

Simple item page
dc.citation.epage10
dc.citation.issue3
dc.citation.journalTitleВимірювальна техніка та метрологія
dc.citation.spage5
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorProdan, Roman
dc.contributor.authorShutka, Denys
dc.contributor.authorTataryn, Vasyl
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2024-03-11T09:15:12Z
dc.date.available2024-03-11T09:15:12Z
dc.date.created2023-02-28
dc.date.issued2023-02-28
dc.description.abstractThe 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.
dc.format.extent5-10
dc.format.pages6
dc.identifier.citationProdan R. Measurement and analysis of agricultural field state using cloud-based data processing pipeline Preventing potential robbery crimes using deep learning algorithm of data processing / Roman Prodan, Denys Shutka, Vasyl Tataryn // Measuring Equipment and Metrology. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 84. — No 3. — P. 5–10.
dc.identifier.citationenProdan R. Measurement and analysis of agricultural field state using cloud-based data processing pipeline Preventing potential robbery crimes using deep learning algorithm of data processing / Roman Prodan, Denys Shutka, Vasyl Tataryn // Measuring Equipment and Metrology. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 84. — No 3. — P. 5–10.
dc.identifier.doidoi.org/10.23939/istcmtm2023.03.005
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/61437
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofВимірювальна техніка та метрологія, 3 (84), 2023
dc.relation.ispartofMeasuring Equipment and Metrology, 3 (84), 2023
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dc.relation.referencesen[1] E. Fukase, W. Martin, "Economic growth, convergence, and world food demand and supply", World Development, Vol. 132, 2020. DOI: https://doi.org/10.1016/j.worlddev.2020.104954
dc.relation.referencesen[2] S. P. Poznyak, "Black soils of Ukraine: geography, genesis and current state", 2016. DOI: https://doi.org/10.15407/ugz2016.01.009
dc.relation.referencesen[3] [3]M. S. Alkatheiri, "Artificial intelligence assisted improved human-computer interactions for computer systems", Computers and Electrical Engineering, Vol. 101, 2022. DOI: https://doi.org/10.1016/j.compeleceng.2022.107950
dc.relation.referencesen[4] H. E. Pence, "What is Big Data and Why is it Important?", Journal of Educational Technology Systems, 43(2), 159–171, 2014. DOI: https://doi.org/10.2190/ET.43.2.d
dc.relation.referencesen[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
dc.relation.referencesen[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
dc.relation.referencesen[7] A. Gupta, P. Goswami, N. Chaudhary, R. Bansal, "Deploying an Application using Google Cloud Platform", 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 2020, 236–239. DOI: https://doi.org/10.1109/ICIMIA48430.2020.9074911
dc.relation.referencesen[8] G. van Dongen, D. van den Poel, "Evaluation of Stream Processing Frameworks", IEEE Transactions on Parallel and Distributed Systems, Vol. 31, No. 8, 1845–1858, 2020. DOI: https://doi.org/10.1109/TPDS.2020.2978480
dc.relation.referencesen[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: https://doi.org/10.1007/978-3-030-42517-3_12
dc.relation.referencesen[10] Z. Dobesova, "Programming language Python for data processing", 2011 International Conference on Electrical and Control Engineering, 2011, 4866–4869. DOI: 10.1109/ICECENG.2011.6057428
dc.relation.referencesen[11] J. Shah and D. Dubaria, "Building Modern Clouds: Using Docker, Kubernetes & Google Cloud Platform", IEEE 9th Annual Computing and Communication Workshop and Conference, 2019, 0184–0189, DOI: https://doi.org/10.1109/CCWC.2019.8666479
dc.relation.referencesen[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 https://doi.org/10.1145/2660267.2660286
dc.relation.referencesen[13] J. Gascon-Samson, F.-P. Garcia, B. Kemme, J. Kienzle, "Dynamoth: A Scalable Pub/Sub Middleware for Latency-Constrained Applications in the Cloud", 2015 IEEE 35th International Conference on Distributed Computing Systems, 2015, 486–496. DOI: https://doi.org/10.1109/ ICDCS.2015.56
dc.relation.referencesen[14] J. Sreemathy, R. Brindha, M. Selva Nagalakshmi, N. Suvekha, N. Karthick Ragul andM. Praveennandha, "Overview of ETL Tools and Talend-Data Integratio", 2021 7th International Conference on Advanced Computing and Communication Systems, 2021, 1650–1654. DOI: https://doi.org/10.1109/ICACCS51430.2021.9441984
dc.relation.referencesen[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, 76–81. DOI: https://doi.org/10.1109/DASA54658.2022.9765168
dc.relation.referencesen[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, 656–660. DOI: https://doi.org/10.1109/IC3I.2016.7918044
dc.relation.referencesen[17] B. Xia, P. Gong, "Review of business intelligence through data analysis", Benchmarking: An International Journal, 21. 300–311, 2014. DOI: https://doi.org/10.1108/BIJ-08-2012-0050.
dc.relation.referencesen[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, 1–7. DOI: https://doi.org/10.1109/ICBDSC.2016.7460347
dc.relation.urihttps://doi.org/10.1016/j.worlddev.2020.104954
dc.relation.urihttps://doi.org/10.15407/ugz2016.01.009
dc.relation.urihttps://doi.org/10.1016/j.compeleceng.2022.107950
dc.relation.urihttps://doi.org/10.2190/ET.43.2.d
dc.relation.urihttps://doi.org/10.1109/ICIMIA48430.2020.9074911
dc.relation.urihttps://doi.org/10.1109/TPDS.2020.2978480
dc.relation.urihttps://doi.org/10.1007/978-3-030-42517-3_12
dc.relation.urihttps://doi.org/10.1109/CCWC.2019.8666479
dc.relation.urihttps://doi.org/10.1145/2660267.2660286
dc.relation.urihttps://doi.org/10.1109/
dc.relation.urihttps://doi.org/10.1109/ICACCS51430.2021.9441984
dc.relation.urihttps://doi.org/10.1109/DASA54658.2022.9765168
dc.relation.urihttps://doi.org/10.1109/IC3I.2016.7918044
dc.relation.urihttps://doi.org/10.1108/BIJ-08-2012-0050
dc.relation.urihttps://doi.org/10.1109/ICBDSC.2016.7460347
dc.rights.holder© Національний університет “Львівська політехніка”, 2023
dc.subjectData Processing
dc.subjectETL
dc.subjectData Analysis
dc.subjectMonitoring and Measurement
dc.subjectInternet of Things
dc.subjectAgriculture
dc.subjectCloud Technologies
dc.titleMeasurement and analysis of agricultural field state using cloud-based data processing pipeline Preventing potential robbery crimes using deep learning algorithm of data processing
dc.typeArticle

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Вимірювальна техніка та метрологія. – 2023. – Випуск 84, №3