From Reality to Virtuality: Revolutionizing Livestock Farming Through Digital Twins

Elanchezhian Arulmozhi, Nibas Chandra Deb, Niraj Tamrakar, Dae Yeong Kang, Myeong Yong Kang, Junghoo Kook, Jayanta Kumar Basak and Hyeon Tae Kim ()
Additional contact information
Elanchezhian Arulmozhi: Department of Bio-Systems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Republic of Korea
Nibas Chandra Deb: Department of Bio-Systems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Republic of Korea
Niraj Tamrakar: Department of Bio-Systems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Republic of Korea
Dae Yeong Kang: Department of Smart Farm, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Republic of Korea
Myeong Yong Kang: Department of Smart Farm, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Republic of Korea
Junghoo Kook: Department of Smart Farm, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Republic of Korea
Jayanta Kumar Basak: Department of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
Hyeon Tae Kim: Department of Bio-Systems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Republic of Korea

Agriculture, 2024, vol. 14, issue 12, 1-22

Abstract: The impacts of climate change on agricultural production are becoming more severe, leading to increased food insecurity. Adopting more progressive methodologies, like smart farming instead of conventional methods, is essential for enhancing production. Consequently, livestock production is swiftly evolving towards smart farming systems, propelled by rapid advancements in technology such as cloud computing, the Internet of Things, big data, machine learning, augmented reality, and robotics. A Digital Twin (DT), an aspect of cutting-edge digital agriculture technology, represents a virtual replica or model of any physical entity (physical twin) linked through real-time data exchange. A DT conceptually mirrors the state of its physical counterpart in real time and vice versa. DT adoption in the livestock sector remains in its early stages, revealing a knowledge gap in fully implementing DTs within livestock systems. DTs in livestock hold considerable promise for improving animal health, welfare, and productivity. This research provides an overview of the current landscape of digital transformation in the livestock sector, emphasizing applications in animal monitoring, environmental management, precision agriculture, and supply chain optimization. Our findings highlight the need for high-quality data, comprehensive data privacy measures, and integration across varied data sources to ensure accurate and effective DT implementation. Similarly, the study outlines their possible applications and effects on livestock and the challenges and limitations, including concerns about data privacy, the necessity for high-quality data to ensure accurate simulations and predictions, and the intricacies involved in integrating various data sources. Finally, the paper delves into the possibilities of digital twins in livestock, emphasizing potential paths for future research and progress.

Keywords: digital twin; livestock management; animal health; precision agriculture; environmental monitoring; supply chain optimization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/12/2231/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/12/2231/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:12:p:2231-:d:1538050

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().