A technical architecture model of agricultural digital twin system based on data elements
Shousen Chen
Shandong Business Institute
Jiliang Guo
Shandong Business Institute
Xuemei Li
Shandong Business Institute
Quanzhu Jiang
Shandong Business Institute
DOI: https://doi.org/10.59429/esta.v12i2.10558
Keywords: New generation information technology; Digital twin system; Data elements
Abstract
This paper presents a technical architecture model for an agricultural digital twin system driven by data elements, aiming to advance agriculture’s digital transformation and enhance productivity and quality. The model integrates IoT, 5G, SaaS, and AI technologies, forming a four-layer structure: perception, transmission, application, and decision-making layers. The perception layer uses sensor networks to collect high-resolution data on crop growth and field environments. The transmission layer supports reliable communication through WLAN, Bluetooth, and ZigBee. The application layer, based on SaaS, provides integrated services for agricultural operations. The decision-making layer utilizes cloud computing and big data analytics to process large datasets. By enabling real-time data acquisition, efficient transmission, and intelligent analysis, the model facilitates accurate mapping and smart management of agricultural activities. It supports data-driven decisions, helping producers optimize resources, reduce risks, and improve efficiency. This architecture not only boosts agricultural productivity and product quality but also fosters sustainable and environmentally friendly practices, offering a robust framework for the intelligent evolution of modern agriculture.
References
[1]Dayıoğlu, M. A., & Turker, U. (2021). Digital transformation for sustainable future-agriculture 4.0: A review. Journal of Agricultural Sciences, 27(4), 373-399.
[2]Elahi, E., Khalid, Z., & Zhang, Z. (2022). Understanding farmers’ intention and willingness to install renewable energy technology: A solution to reduce the environmental emissions of agriculture. Applied Energy, 309, 118459.
[3]Gour, B., & Maurya, J. P. (2023). Intelligent and Smart Agriculture System Using Cooperative Approach. In Intelligent Sensor Node-Based Systems (pp. 211-228). Apple Academic Press.
[4]Huang Chenglong, Ke Yuxi, Hua Xiangdong, et al. Application status and prospect of edge computing in smart agriculture [J]. Journal of Agricultural Engineering, 2022, 38 (16): 224-234
[5]Martinho, V. J. P. D., & Guine, R. D. P. F. (2021). Integrated-smart agriculture: contexts and assumptions for a broader concept. Agronomy, 11(8), 1568.