Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM.NCET007
Volume:
10 |
10 |
Oct 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Applied Electronics
Nexzon Enterprises
Digital Twin technology in agriculture primarily focuses on crop monitoring, irrigation, and resource optimization, while agricultural knowledge preservation and transfer remain less explored. This paper proposes EduTwin, a unified Digital Twin architecture that treats agricultural knowledge as a core digital asset alongside farm data. The proposed methodology integrates knowledge capture, digital representation, simulation, and multi-level learning for school students, college learners, engineering students, and experienced farmers. Traditional farming expertise and modern agricultural practices are represented within a common environment, enabling comparative learning and what-if decision simulation. Conceptual analysis indicates that EduTwin unifies agricultural knowledge preservation, tacit knowledge capture, multi-audience learning, and cross-institutional knowledge sharing through a shared simulation architecture. The framework thus proposes a knowledge-centric Digital Twin model that extends conventional farm monitoring toward sustainable agricultural education and long-term preservation of farming expertise.
KEYWORDS
The application of digital twin technology in agriculture has largely been limited to optimizing agricultural operations, while its use in the preservation and transmission of knowledge has received relatively little attention. This paper has proposed EduTwin, a framework where a digital twin, using the same IoT data as any other agricultural DT system, will in addition serve six purposes related to learning and knowledge retention through a shared simulator. In light of the literature surveyed in this paper, EduTwin could be considered a conceptual framework that attempts to solve the problems of multi-audience learning, tacit knowledge, live data to teaching, and cross-institutional design. While most of the framework elements need empirical testing in order to assess their value, and while certain elements pose governance and methodology problems that have been highlighted rather than solved in this paper, EduTwin suggests a viewpoint that does not seem to have gained sufficient traction in contemporary research on digital twins in agriculture, namely treating the DT not only as an operational monitor but as a tool for preserving and transmitting knowledge from farmer to farmer. Moving towards such a view through pilot work, as outlined in Section IV, may be considered the next step in this research line.
[1] of-the-art review," Smart Agricultural Technology, vol. 3, p. 100094, 2023. [2] N. Peladarinos et al., "Enhancing smart agriculture by implementing digital twins: A comprehensive review," Sensors, vol. 23, no. 16, p. 7128, 2023. [3] P. Kumar, G. Kumar, and N. Kaur, "Digital Twin Implementation in Agriculture: Strategies Leveraging AI and IoT," in Digital Twin Technology for Sustainable Agriculture, A. L. Srivastav, N. Kumar, and I. Zinicovscaia, Eds. Singapore: Springer, 2026, doi: 10.1007/978-981-95-5915-2_20. [4] Q. Xie et al., "Recent advances in the digital twin system for smart farming: A five-year review," in Proc. 2024 Int. Conf. CIITE, IEEE, 2024, pp. 1-6. [5] Y. A. Reyes, R. Abbasi, and P. Martinez, "Digital twinning of hydroponic grow beds in intelligent aquaponic systems," Sensors, vol. 22, no. 19, p. 7393, 2022. [6] A. Semin et al., "Improving the theoretical and methodological framework for implementing digital twin technology in agriculture," Emerging Science J., vol. 7, no. 4, pp. 1100-1115, 2023. [7] A. Subeesh and N. Chauhan, "Agricultural digital twin for smart farming: A review," Green Technologies and Sustainability, art. 100299, 2025. [8] M. Tsaousidis, T. Kalampokas, E. Vrochidou, and G. A. Papakostas, "AI-Enabled Digital Twins in Agriculture," AI, vol. 7, no. 3, art. 108, 2026, doi: 10.3390/ai7030108. [9] A. Adeyemi, S. Ma, X. Zhihong, and R. Quijada-Landaverde, "Outcome-based use of simulation in agricultural sciences: A systematic literature review," NACTA J., vol. 68, no. 1, 2024, doi: 10.56103/nactaj.v68i1.166. [10] J. Pulley, K. Claflin, and A. Thompson, "A review of virtual reality applications in agriculture education and recommendations for future research," J. Agricultural Education, vol. 66, no. 3, art. 15, 2025, doi: 10.5032/jae.v66i3.3195.
Publication Date
10/5/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
2
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...