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Journals / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2026 / Cilt: 15 Sayı: 1

TarBIoT DSS: Real-time Decision Support System Design with Blockchain and IoT Technologies for Smart Agriculture Applications

TarBIoT KDS: Akıllı Tarım Uygulamaları için Blokzincir ve IoT teknolojileri ile Gerçek zamanlı Karar Destek Sistemi Tasarımı

Doruk Ayberkin(BAYBURT ÜNİVERSİTESİ, UYGULAMALI BİLİMLER FAKÜLTESİ, YÖNETİM BİLİŞİM SİSTEMLERİ BÖLÜMÜ, YÖNETİM BİLİŞİM SİSTEMLERİ PR.)0000-0003-3409-8926
Pages
12–24
Publication Date
2025-03-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings indicate that the TarBIoT system can serve as a pivotal element in advancing intelligent agricultural techniques, enhancing food security, and advancing sustainable agricultural practices.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study introduces a novel methodology for addressing global challenges in the agricultural sector.
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Key Findings & Data:This integrated structure has the potential to enhance efficiency in agricultural production, optimize the utilization of resources, and facilitate environmental sustainability.
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Conclusion & Impact:The findings indicate that the TarBIoT system can serve as a pivotal element in advancing intelligent agricultural techniques, enhancing food security, and advancing sustainable agricultural practices.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Akıllı tarım · Blokzincir teknolojisi · Nesnelerin İnterneti

Abstract

This study introduces a novel methodology for addressing global challenges in the agricultural sector. The research proposes a comprehensive system for the collection, secure storage, and analysis of agricultural data through the integration of blockchain and Internet of Things (IoT) technologies. The system, designated TarBIoT, furnishes a decision-support apparatus for farmers and other stakeholders, processing soil and climate data from assorted sensors in real-time. The system comprises several key components, including an IoT sensor network, a blockchain infrastructure, a data processing and analysis module, and a decision support system. This integrated structure has the potential to enhance efficiency in agricultural production, optimize the utilization of resources, and facilitate environmental sustainability. The transparency and reliability of blockchain technology enable the secure sharing of data among all stakeholders, while the decision support system transforms this data into meaningful and actionable information. The study employs a simulation to assess the efficacy of the proposed system in monitoring and analyzing pivotal agricultural parameters, including water level, moisture content, and soil pH. The findings indicate that the TarBIoT system can serve as a pivotal element in advancing intelligent agricultural techniques, enhancing food security, and advancing sustainable agricultural practices. This research has the potential to expedite the digital transformation in the agricultural sector and promote data-driven agricultural practices.

Özet

Bu çalışma, tarım sektöründeki küresel zorlukları ele almak için yenilikçi bir yaklaşım sunmaktadır. Araştırma, blokzincir ve Nesnelerin İnterneti (IoT) teknolojilerini entegre ederek, tarımsal verilerin toplanması, güvenli bir şekilde depolanması ve analiz edilmesi için kapsamlı bir sistem önermektedir. TarBIoT olarak adlandırılan bu sistem, çeşitli sensörlerden elde edilen toprak ve iklim verilerini gerçek zamanlı olarak işleyerek, çiftçilere ve diğer paydaşlara yönelik bir karar destek mekanizması sunmaktadır. Sistemin temel bileşenleri arasında IoT sensör ağı, blokzincir altyapısı, veri işleme ve analiz modülü ile karar destek sistemi bulunmaktadır. Bu entegre yapı, tarımsal üretimde verimliliği artırma, kaynakları optimize etme ve çevresel sürdürebilirliği destekleme potansiyeli taşımaktadır. Blokzincir teknolojisinin sağladığı şeffaflık ve güvenilirlik, verilerin tüm paydaşlar arasında güvenli bir şekilde paylaşılmasına olanak tanırken, karar destek sistemi, bu verileri anlamlı ve uygulanabilir bilgilere dönüştürmektedir. Çalışma, önerilen sistemin simülasyonunu gerçekleştirerek, su seviyesi, nem oranı, toprak pH değeri gibi kritik tarımsal parametrelerin izlenmesi ve analiz edilmesindeki etkinliğini değerlendirmektedir. Sonuçlar, TarBIoT sisteminin, akıllı tarım uygulamalarının geliştirilmesinde, gıda güvenliğinin artırılmasında ve sürdürülebilir tarım pratiklerinin yaygınlaştırılmasında önemli bir rol oynayabileceğini göstermektedir. Bu araştırma, tarım sektöründeki dijital dönüşümü hızlandırma ve veri odaklı tarım uygulamalarını yaygınlaştırma potansiyeli taşımaktadır.

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Cite This Article

Ayberkin, D. (2025). TarBIoT DSS: Real-time Decision Support System Design with Blockchain and IoT Technologies for Smart Agriculture Applications. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 15 Sayı: 1, 12–24. https://doi.org/10.21597/jist.1549363

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References (29)

  1. Ayberkin, D., ve Özen, Ü. (2021). Blokzincir Teknolojisinin Dijital Reklam Ve Pazarlama Sektöründe Kullanımı: Modelleme Çalışması Ve Kavramsal Bir Çerçeve. Dijital Çağda İşletmecilik Dergisi, 4(2), 165–171. https://doi.org/10.46238/JOBDA.1021911
  2. Benos, Lefteris, Aristotelis C. Tagarakis, Georgios Dolias, Remigio Berruto, Dimitrios Kateris, ve Dionysis Bochtis. 2021. “Machine Learning in Agriculture: A Comprehensive Updated Review”. Sensors 2021, Vol. 21, Page 3758 21(11):3758. doi: 10.3390/S21113758.Chlingaryan, A., Sukkarieh, S., ve Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture, 151, 61–69. https://doi.org/10.1016/J.COMPAG.2018.05.012
  3. Crosby, M., Pattanayak, P., Verma, S., ve Kalyanaraman, V. (2016). Applied Innovation Review. Applied Innovation Review, 2, 5–20.
  4. Eby, P. J. (2010). Python Web Server Gateway Interface. Python Enhancement Proposals. https://peps.python.org/pep-3333/
  5. Fernández-Caramés, T. M., ve Fraga-Lamas, P. (2018). A Review on the Use of Blockchain for the Internet of Things. IEEE Access, 6, 32979–33001. https://doi.org/10.1109/ACCESS.2018.2842685
  6. Ferrández-Pastor, F. J., Mora-Pascual, J., ve Díaz-Lajara, D. (2022). Agricultural traceability model based on IoT and Blockchain: Application in industrial hemp production. Journal of Industrial Information Integration, 29, 100381. https://doi.org/10.1016/J.JII.2022.100381
  7. Gupta, M., Abdelsalam, M., Khorsandroo, S., ve Mittal, S. (2020). Security and Privacy in Smart Farming: Challenges and Opportunities. IEEE Access, 8, 34564–34584. https://doi.org/10.1109/ACCESS.2020.2975142
  8. Huong, T. T., Huu Thanh, N., Van, N. T., Tien Dat, N., Long, N. Van, ve Marshall, A. (2018). Water and energy-efficient irrigation based on markov decision model for precision agriculture. 2018 IEEE 7th International Conference on Communications and Electronics, ICCE 2018, 51–56. https://doi.org/10.1109/CCE.2018.8465723
  9. Ingle, A. (2020). Crop Recommendation Dataset. Kaggle. https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset
  10. Jagtap, Santosh T., Khongdet Phasinam, Thanwamas Kassanuk, Subhesh Saurabh Jha, Tanmay Ghosh, ve Chetan M. Thakar. 2022. “Towards application of various machine learning techniques in agriculture”. Materials Today: Proceedings 51:793–97. doi: 10.1016/J.MATPR.2021.06.236.
  11. Jensen, T., Apan, A., ve Zeller, L. (2009). Crop maturity mapping using a low-cost low-altitude remote sensing system. Içinde P. Ostendorf, Bertram, Baldock, Penny, Bruce, David, Burdett, Michael and Corcoran (Ed.), Proceedings of the 2009 Surveying and Spatial Sciences Institute Biennial International Conference (SSC 2009) (s. 13). https://research.usq.edu.au/item/9z98v/crop-maturity-mapping-using-a-low-cost-low-altitude-remote-sensing-system
  12. Kamilaris, A., Fonts, A., ve Prenafeta-Boldύ, F. X. (2019). The rise of blockchain technology in agriculture and food supply chains. Trends in Food Science ve Technology, 91, 640–652. https://doi.org/10.1016/J.TIFS.2019.07.034
  13. Kassanuk, T., ve Phasinam, K. (2022). Design of blockchain based smart agriculture framework to ensure safety and security. Materials Today: Proceedings, 51, 2313–2316. https://doi.org/10.1016/J.MATPR.2021.11.415
  14. Khanal, S., Fulton, J., ve Shearer, S. (2017). An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture, 139, 22–32. https://doi.org/10.1016/J.COMPAG.2017.05.001
  15. Lasi, H., Fettke, P., Kemper, H. G., Feld, T., ve Hoffmann, M. (2014). Industry 4.0. Business and Information Systems Engineering, 6(4), 239–242. https://doi.org/10.1007/S12599-014-0334-4/FIGURES/1
  16. McBratney, A., Whelan, B., Ancev, T., ve Bouma, J. (2005). Future directions of precision agriculture. Precision Agriculture, 6(1), 7–23. https://doi.org/10.1007/s11119-005-0681-8
  17. Munir, M. S., Bajwa, I. S., ve Cheema, S. M. (2019). An intelligent and secure smart watering system using fuzzy logic and blockchain. Computers ve Electrical Engineering, 77, 109–119. https://doi.org/10.1016/J.COMPELECENG.2019.05.006
  18. Nageswara Rao, R., ve Sridhar, B. (2018). IoT based smart crop-field monitoring and automation irrigation system. Proceedings of the 2nd International Conference on Inventive Systems and Control, ICISC 2018, 478–483. https://doi.org/10.1109/ICISC.2018.8399118
  19. Nandurkar, S. R., Thool, V. R., ve Thool, R. C. (2014). Design and development of precision agriculture system using wireless sensor network. 1st International Conference on Automation, Control, Energy and Systems - 2014, ACES 2014. https://doi.org/10.1109/ACES.2014.6808017
  20. Navarro-Hellín, H., Martínez-del-Rincon, J., Domingo-Miguel, R., Soto-Valles, F., ve Torres-Sánchez, R. (2016). A decision support system for managing irrigation in agriculture. Computers and Electronics in Agriculture, 124, 121–131. https://doi.org/10.1016/J.COMPAG.2016.04.003
  21. Özer, B., Kuş, S., Yildiz, O., Üniversitesi, G., Enstitüsü, B., Sistemleri, B., Ankara, T., Fakültesi, M., Mühendisliği, B., Anahtar, K., Öz, A., Tarım, V., Madenciliği, V., Analizi, C., ve Bilgi, S. (2022). VERİ MADENCİLİĞİ YÖNTEMLERİ İLE TARIMSAL VERİ ANALİZİ: BİR AKILLI TARIM SİSTEMİ ÖNERİSİ. Journal of Engineering Sciences and Design, 10(4), 1417–1429. https://doi.org/10.21923/JESD.1081814
  22. Patil, A. S., Tama, B. A., Park, Y., ve Rhee, K. H. (2018). A Framework for Blockchain Based Secure Smart Green House Farming. Lecture Notes in Electrical Engineering, 474, 1162–1167. https://doi.org/10.1007/978-981-10-7605-3_185
  23. Rupnik, R., Kukar, M., Vračar, P., Košir, D., Pevec, D., ve Bosnić, Z. (2019). AgroDSS: A decision support system for agriculture and farming. Computers and Electronics in Agriculture, 161, 260–271. https://doi.org/10.1016/J.COMPAG.2018.04.001
  24. Sahoo, S., T. A. Russo, J. Elliott, ve I. Foster. 2017. “Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.” Water Resources Research 53(5):3878–95. doi: 10.1002/2016WR019933.
  25. Suraya, S., ve Sholeh, M. (2022). Designing and Implementing a Database for Thesis Data Management by Using the Python Flask Framework. International Journal of Engineering, Science and Information Technology, 2(1), 9–14. https://doi.org/10.52088/IJESTY.V2I1.197
  26. Tiwari, A., Sadistap, S., ve Mahajan, S. K. (2018). Development of Environment Monitoring System Using Internet of Things. Advances in Intelligent Systems and Computing, 696, 403–412. https://doi.org/10.1007/978-981-10-7386-1_35
  27. Torky, M., ve Hassanein, A. E. (2020). Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges. Computers and Electronics in Agriculture, 178, 105476. https://doi.org/10.1016/J.COMPAG.2020.105476
  28. Upadhyay, A., Mukhuty, S., Kumar, V., ve Kazancoglu, Y. (2021). Blockchain technology and the circular economy: Implications for sustainability and social responsibility. Journal of Cleaner Production, 293, 126130. https://doi.org/10.1016/J.JCLEPRO.2021.126130
  29. Zhai, Z., Martínez, J. F., Beltran, V., ve 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