Vol. 3 No. 1 (2024): The QUEST: Journal of Multidisciplinary Research and Development
Articles

5G Internet of Things (IoT) And Its Impact to Agricultural Technology: Basis for Strategic Plan

  • Xiaopeng Li,
  • Jet Aquino
Xiaopeng Li
Zongjie Technology (Hainan) Co., Ltd., China
Jet Aquino
Nueva Ecija University of Science and Technology
DOI: https://doi.org/10.60008/thequest.v3i1.172

Published 06/30/2024

Keywords

  • 5G-enabled IoT,
  • Agricultural Technology,
  • Stakeholders Profile,
  • Connectivity,
  • Access,
  • Technical Requirements
    • ...More
    Less

How to Cite

Li, X., & Aquino, J. (2024). 5G Internet of Things (IoT) And Its Impact to Agricultural Technology: Basis for Strategic Plan. The QUEST: Journal of Multidisciplinary Research and Development, 3(1). https://doi.org/10.60008/thequest.v3i1.172

Copyright (c) 2024 The QUEST: Journal of Multidisciplinary Research and Development

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

This study explored the integration and impact of 5G-enabled Internet of Things (IoT) technology in agricultural practices in Hainan Province, China, through a quantitative descriptive approach. The study encompasses responses from the participants, including farm managers, agricultural technology experts, and farm owners. It delves into the demographic and professional profiles of these respondents, revealing a majority in the 35-44 age group, predominantly female, with a significant number holding college degrees or higher and mostly farm owners. The impact of 5G-enabled IoT on agricultural technology is generally perceived positively by respondents. The shared understanding of 5G's potential positive influence on agricultural productivity, compatibility with existing setups, and operational performance underscores the transformative capabilities of this technology.  However, the study also identifies challenges, including concerns regarding cost effectiveness, accuracy, and risk/security, which pose significant hurdles to the widespread adoption of these technologies. The proposed strategic plan suggests targeted outreach, awareness programs, and the development of innovative financing models and cybersecurity measures to foster a conducive environment for the adoption of 5G IoT in agriculture. In conclusion, the study provides insights into the current perception and impact of 5G-enabled IoT in agriculture, coupled with actionable recommendations to navigate the challenges and maximize the technology's potential benefits. This comprehensive approach aims to support stakeholders in the agricultural sector in leveraging 5G IoT for enhanced productivity and sustainability.

Full Paper

References

  1. Ang, Y., Sathian, D., Hou, C., Guo, Q., Shaoming, L., & Yong, H. (2021). A survey on the 5G network and its impact on agriculture: Challenges and opportunities. Computers and Electronics in Agriculture, doi: 10.1016/J.COMPAG.2020.105895
  2. Atalla, S., Tarapiah, S., Gawanmeh, A., Muhsen, M., Daradkeh, M., Mukhtar, H., Himeur, Y., Mansoor, W., Hashim, K. F., & Daadoo, M. (2023). IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management. Information, doi: 10.3390/info14040205
  3. Aydemir, M., & Cengiz, K. (2017). Emerging infrastructure and technology challenges in 5G wireless networks.
  4. Balaji, S., Shrimali, T., Shankar, T. P., & Santhakumar, R. (2023). Precision Agriculture Crop Recommendation System Using IoT and Machine Learning. doi: 10.1109/ViTECoN58111.2023.10157577
  5. Chehri, A., et al. (2020). A framework for optimizing the deployment of IoT for the precision agriculture industry. Procedia Computer Science, doi: 10.1016/J.PROCS.2020.09.312
  6. Di Palma, D., Bencini, L., Collodi, G., Manes, G., Fantacci, R., & Manes, A. (2010). Distributed Monitoring Systems for Agriculture based on Wireless Sensor Network Technology.
  7. Goss, M. J., Carvalho, M., & Brito, I. (2017). Challenges to Agriculture Systems. doi: 10.1016/B978-0-12-804244-1.00001-0
  8. Guo, J., Wang, L., Zhou, W., & Wei-Kan, C. (2022). Powering green digitalization: Evidence from 5 G network infrastructure in China. Resources Conservation and Recycling, doi: 10.1016/j.resconrec.2022.106286
  9. Guo, X. (2021). Application of agricultural IoT technology based on 5 G network and FPGA. Microprocessors and Microsystems, doi: 10.1016/J.MICPRO.2020.103597
  10. Hanis, N., Sabirin, A., Fadhil, N. F. M., & Arifin, J. (2022). Information Technology (IT) in the Agriculture Sector: Issues and Challenges. Social and management research journal, doi: 10.24191/smrj.v19i2.19307
  11. Ila, K., & Nupur, P. (2021). Applicability of IoT for Smart Agriculture: Challenges & Future Research Direction. doi: 10.1109/AIIOT52608.2021.9454209
  12. Jesi, V. E., et al. (2022). IoT Enabled Smart Irrigation and Cultivation Recommendation System for Precision Agriculture. ECS transactions, doi: 10.1149/10701.5953ecst
  13. Kaun, E. S. S., Cruz, A. W., Cunha, M. A., Proença, T. P. M., Marques, S., & da Silva, E. D. (2021). Cost-effectiveness in health: consolidated research and contemporary challenges. Palgrave Communications, doi: 10.1057/S41599-021-00940-5
  14. Kumar, R., et al. (2022). IoT Enabled Technologies in Smart Farming and Challenges for Adoption. doi: 10.1007/978-981-16-6210-2_7
  15. Kumar, R., et al. (2022). IoT Enabled Technologies in Smart Farming and Challenges for Adoption. doi: 10.1007/978-981-16-6210-2_7
  16. Martínez-Ortega, J. F., Díaz, V. H., & Martínez, N. L. (2023). Big Data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability. Journal of Big Data, doi: 10.1186/s40537-023-00729-0
  17. Martínez-Ortega, J. F., Díaz, V. H., & Martínez, N. L. (2023). Big Data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability. Journal of Big Data, doi: 10.1186/s40537-023-00729-0
  18. Mea, Y.-S. (2016). Impact of connectivity on sustainable development.
  19. Mea, Y.-S. (2016). Impact of connectivity on sustainable development.
  20. Mentsiev, A. U., & Gatina, F. F. (2021). Data analysis and digitalisation in the agricultural industry. doi: 10.1088/1755-1315/677/3/032101
  21. Mentsiev, A. U., & Gatina, F. F. (2021). Data analysis and digitalisation in the agricultural industry. doi: 10.1088/1755-1315/677/3/032101
  22. Mishra, V. (2023). Enhancing Crop Yields through IoT-Enabled Precision Agriculture. doi: 10.1109/ICDT57929.2023.10151422
  23. Mishra, V. (2023). Enhancing Crop Yields through IoT-Enabled Precision Agriculture. doi: 10.1109/ICDT57929.2023.10151422
  24. Naqvi, S. Z., et al. (2022). Role of 5G and 6G Technology in Precision Agriculture. doi: 10.3390/environsciproc2022023003
  25. Naqvi, S. Z., et al. (2022). Role of 5G and 6G Technology in Precision Agriculture. doi: 10.3390/environsciproc2022023003
  26. Ntihemuka, M., & Inoue, M. (2018). IoT Monitoring System for Early Detection of Agricultural Pests and Diseases. doi: 10.1109/SEATUC.2018.8788860
  27. Ntihemuka, M., & Inoue, M. (2018). IoT Monitoring System for Early Detection of Agricultural Pests and Diseases. doi: 10.1109/SEATUC.2018.8788860
  28. Padmalaya, N., K., K., & Ch., M. R. (2020). IoT-Enabled Agricultural System Applications, Challenges and Security Issues. doi: 10.1007/978-981-13-9177-4_7
  29. Padmalaya, N., K., K., & Ch., M. R. (2020). IoT-Enabled Agricultural System Applications, Challenges and Security Issues. doi: 10.1007/978-981-13-9177-4_7
  30. Ramsey, S. D. (2002). Cost effectiveness: con. Amyotrophic Lateral Sclerosis, doi: 10.1080/146608202320374336
  31. Ramsey, S. D. (2002). Cost effectiveness: con. Amyotrophic Lateral Sclerosis, doi: 10.1080/146608202320374336
  32. Raneesha, A., Madushanki, N., Malka, Halgamuge, W., Surangi, A.H., Wirasagoda, Ali, S. (2019). Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review. International Journal of Advanced Computer Science and Applications, doi: 10.14569/IJACSA.2019.0100402
  33. Rishabh, R., & Avinash, A. (2022). IoT in Farm Productivity Enhancement. doi: 10.1109/DASA54658.2022.9765273
  34. Rishabh, R., & Avinash, A. (2022). IoT in Farm Productivity Enhancement. doi: 10.1109/DASA54658.2022.9765273
  35. Scott, D. R. (2002). Cost effectiveness: con. Amyotrophic Lateral Sclerosis, doi: 10.1080/146608202320374336
  36. Scott, D. R. (2002). Cost effectiveness: con. Amyotrophic Lateral Sclerosis, doi: 10.1080/146608202320374336
  37. Senapaty, M. K., Ray, A., & Padhy, N. (2023). IoT-Enabled Soil Nutrient Analysis and Crop Recommendation Model for Precision Agriculture. Computers, doi: 10.3390/computers12030061
  38. Senapaty, M. K., Ray, A., & Padhy, N. (2023). IoT-Enabled Soil Nutrient Analysis and Crop Recommendation Model for Precision Agriculture. Computers, doi: 10.3390/computers12030061
  39. Shoba, M., et al. (2022). Survey on IoT based E-Farming Technology Enabled Farming. doi: 10.1109/ICSCDS53736.2022.9760870
  40. Shoba, M., et al. (2022). Survey on IoT based E-Farming Technology Enabled Farming. doi: 10.1109/ICSCDS53736.2022.9760870
  41. Shu-Ching, W., Wei-Ling, L., Chun-Hung, H., Mao-Lun, C., & Tung-Shou, C. (2021). The enhancement of agricultural productivity using the intelligent IoT. International Journal of Applied Science and Engineering, doi: 10.6703/IJASE.202103_18(1).005
  42. Shu-Ching, W., Wei-Ling, L., Chun-Hung, H., Mao-Lun, C., & Tung-Shou, C. (2021). The enhancement of agricultural productivity using the intelligent IoT. International Journal of Applied Science and Engineering, doi: 10.6703/IJASE.202103_18(1).005
  43. Sureshkumar, P. (2023). Review on the Real-time Implementation of IoT-enabled UAV in Precision Agriculture and the Overview of Collision Avoidance Strategies. The Philippine journal of science, doi: 10.56899/152.03.29
  44. Sureshkumar, P. (2023). Review on the Real-time Implementation of IoT-enabled UAV in Precision Agriculture and the Overview of Collision Avoidance Strategies. The Philippine journal of science, doi: 10.56899/152.03.29
  45. Tang, Y., Sathian, D., Hou, C., Guo, Q., Shaoming, L., & Yong, H. (2021). A survey on the 5G network and its impact on agriculture: Challenges and opportunities. Computers and Electronics in Agriculture, doi: 10.1016/J.COMPAG.2020.105895
  46. Tang, Y., Sathian, D., Hou, C., Guo, Q., Shaoming, L., & Yong, H. (2021). A survey on the 5G network and its impact on agriculture: Challenges and opportunities. Computers and Electronics in Agriculture, doi: 10.1016/J.COMPAG.2020.105895
  47. Thirumagal, P. G., et al. (2023). IoT and Machine Learning Based Affordable Smart Farming. doi: 10.1109/ICONSTEM56934.2023.10142329
  48. Thirumagal, P. G., et al. (2023). IoT and Machine Learning Based Affordable Smart Farming. doi: 10.1109/ICONSTEM56934.2023.10142329
  49. Weinert, B., & Uslar, M. (2020). Challenges for System of Systems in the Agriculture Application Domain. doi: 10.1109/SOSE50414.2020.9130552
  50. Weinert, B., & Uslar, M. (2020). Challenges for System of Systems in the Agriculture Application Domain. doi: 10.1109/SOSE50414.2020.9130552
  51. Weinstein, M. C. (1986). Challenges for cost-effectiveness research. Medical Decision Making, doi: 10.1177/0272989X8600600402
  52. Weinstein, M. C. (1986). Challenges for cost-effectiveness research. Medical Decision Making, doi: 10.1177/0272989X8600600402
  53. Zaigham, S., Naqvi, A., Saleem, S., Tahir, M. N., Li, S., Hussain, S., Haq, S. I. U., & Awais, M. (2022). Role of 5G and 6G Technology in Precision Agriculture. doi: 10.3390/environsciproc2022023003
  54. Zaigham, S., Naqvi, A., Saleem, S., Tahir, M. N., Li, S., Hussain, S., Haq, S. I. U., & Awais, M. (2022). Role of 5G and 6G Technology in Precision Agriculture. doi: 10.3390/environsciproc2022023003