Evaluation on the Usage and Future of 5G Technologies in Smart Agricultural Systems

Year 2022, , 81 - 86, 01.04.2022

Dilek Çakır , Mehmet Serhat Odabas , Gökhan Kayhan , Recai Oktaş

https://doi.org/10.34248/bsengineering.1051374

Abstract

Nutrition, which is one of the first human needs, emphasizes the importance of agriculture. However, the ever-increasing human population and the decrease in agricultural areas necessitated the necessity of different approaches in agriculture. For this reason, research was conducted to improve the sustainability of agriculture and its productivity by using mobile internet connections, the internet of things (IoT), and artificial intelligence (AI) in the agricultural sector. This study attempts to distinguish 5G technology by evaluating the approaches to innovations in the field of agriculture between 2017 and 2021 in terms of technology. Thanks to the high-speed data transmission provided by 5G, a study has been carried out to provide sustainability and increase efficiency by saving time, cost, and labor in a more comprehensive, accurate, and timely manner in harvesting, weed detection, irrigation, and spraying applications with real-time data transmission.

Keywords

5G, Smart Agriculture Systems, Technology, Future

5G Teknolojilerinin Akıllı Tarım Sistemlerinde Kullanımı ve Geleceği Üzerine Değerlendirme

Abstract

İnsan ihtiyaçlarından ilk sıralarda yer alan beslenme tarımın önemini vurgulamaktadır. Ancak sürekli artan insan popülasyonu ve tarım yapılan alanların azalması tarımda farklı yaklaşımların gerekliliğini zorunlu kılmıştır. Bu gerekçe ile tarım sektöründe yapay zekâ (AI), nesnelerin interneti (IoT) ve mobil internet bağlantıları kullanılarak tarımın ve veriminin artırılması sürdürülebilirliğinin sağlanması için çalışmalar yapılmaktadır. Bu araştırmada tarım alanında yapılan yeniliklere 2017 ile 2021 yılları arasında teknoloji açısından yaklaşımları değerlendirilerek Beşinci Nesil Mobil İletişim (5G) teknolojisinin ayrımına varılmaya çalışılmıştır. 5G’nin sağladığı yüksek hızlı veri iletimi sayesinde gerçek zamanlı veri iletimi ile hasat, yabancı ot tespiti, sulama, ilaçlama uygulamalarında daha kapsamlı, doğru ve zaman, maliyet, iş gücü konularında tasarruf edilerek sürdürülebilirliğin sağlanması ve verimin artırılması öngörülmesi üzerine çalışma yapılmıştır.

Keywords

5G, Akıllı Tarım Sistemleri, Teknoloji, Gelecek

References

• Al-Sammarraie MAJ, Ali AA, Hussein NM. 2021. New irrigation techniques for precision agriculture: A Review. Plant Archives, 21(1): 1734-1740.
• Andrews JG, Buzzi S, Choi W, Hanly SV, Lozano A, Soong AC, Zhang JC. 2014. What will 5G be? IEEE J Commun, 32(6): 1065-1082.
• Campbell K, Diffley J, Flanagan B, Morelli B, O’Neil B, Sideco F. 2017. The 5G economy: How 5G technology will contribute to the global economy. IHS Econ IHS Technol, 4: 16.
• Elijah O, Orikumhi I, Rahman TA, Babale SA, Orakwue SI. 2017. Enabling smart agriculture in Nigeria: Application of IoT and data analytics. IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON), 7-10 Nov. 2017. DOI: 0.1109/NIGERCON.2017.8281944.
• Foster T, Mieno T, Brozović N. 2020. Satellite‐based monitoring of irrigation water use: assessing measurement errors and their implications for agricultural water management policy. Water Res Res, 56(11): e2020WR028378.
• Gökçe G, Goncu S, Bozkurt S. 2020. Endüstri 4.0 ve hayvancılık. Uluslararası Anadolu Ziraat Müh Bil Derg, 2(3): 21-26.
• Haar JM, de Fluiter A, Brougham D. 2016. Abusive supervision and turnover intentions: The mediating role of perceived organisational support. J Manag Organizat, 22(2): 139-153.
• Joshua L, Varghese K. 2014. Automated recognition of construction labour activity using accelerometers in field situations. Int J Product Perform Manag, 67(3): 841-862.
• Kılavuz E, Erdem İ. 2019. Dünyada tarım 4.0 uygulamaları ve Türk tarımının dönüşümü. Soc Sci, 14(4): 133-157.
• Kitouni I, Benmerzoug D, Lezzar F. 2018. Smart agricultural enterprise system based on integration of internet of things and agent technology. J Organiz End User Comput, 30(4): 64-82.
• Li T, Li D. 2020. Prospects for the application of 5G technology in agriculture and rural areas. 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), December 25-27, 2020, Harbin, China, pp: 2176-2179.
• O'Grady MJ, Langton D, O'Hare GMP. 2019. Edge computing: A tractable model for smart agriculture? Artificial Intell Agri, 3: 42-51. DOI: 10.1016/j.aiia.2019.12.001.
• Oliveira LF, Moreira AP, Silva MF. 2021. Advances in agriculture robotics: A state-of-the-art review and challenges ahead. Robotics, 10(2): 52.
• Rao SK, Prasad R. 2018. Impact of 5G technologies on industry 4.0. Wireless Pers Commun, 100(1): 145-159.
• Routray SK, Tengshe R, Javali A, Sarkar S, Sharma L, Ghosh AD. 2019. Satellite based iot for mission critical applications. International Conference on Data Science and Communication (IconDSC), March 1-2, 2019, angalore, India, DOI: 10.1109/IconDSC.2019.8817030.
• Shafi U, Mumtaz R, García-Nieto J, Hassan SA, Zaidi SAR, Iqbal N. 2019. Precision agriculture techniques and practices: From considerations to applications. Sensors, 19(17): 3796.
• Su Y, Wang X. 2021. Innovation of agricultural economic management in the process of constructing smart agriculture by big data. Sust Comput Infor Syst, 31: 100579.
• Tang Y, Dananjayan S, Hou C, Guo Q, Luo S, He Y. 2021. A survey on the 5G network and its impact on agriculture: Challenges and opportunities. Comp Elect Agri, 180: 105895.
• Tekin A. 2018. Tarımsal üretimin geleceği: akıllı tarım. Türktob Derg, (26): 26-27.
• Tyrychtr J, Vostrovsky V. 2017. The current state of the issue of information needs and dispositions among small Czech farms. Agri Econ, 63(4): 164-174.
• Uddin MA, Ayaz M, Mansour A, Sharif Z, Razzak I. 2021. Cloud-connected flying edge computing for smart agriculture. Peer-to-Peer Networking Appl, 14: 3405-3415
• Uzun Y, Bilban M, Arıkan A, 2018. Hassas tarım ve kırsal kalkınmada yapay zekâ kullanımı. VI. KOP Bölgesel Kalkınma Sempozyumu, KOPBKS 26-28 Ekim 2018, Konya, Turkey, pp: 68.
• Weerasinghe IPT, Ruwanpura JY. 2009. Automated data acquisition system to assess construction worker performance. Construction Research Congress 2009: Building a Sustainable Future, April 5-7, 2009, Seattle, Washington, US, DOI: 10.1061/41020(339)7.
• Yazdani M, Zarate P, Coulibaly A, Zavadskas EK. 2017. A group decision making support system in logistics and supply chain management. Expert Syst Appl, 88: 376-392.
• Zhai Z, Martínez JF, Beltran V, Martínez NL. 2020. Decision support systems for agriculture 4.0: Survey and challenges. Comp Elect Agri, 170: 105256.