A multi-layered digital twin framework for smart agriculture and livestock: System architecture and implementation challenges
Küçük Resim Yok
Tarih
2026
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Context: Agriculture and livestock systems face mounting pressure from climate change, resource scarcity, and population growth, increasing the need for intelligent, data-driven management. Digital twin (DT) technology-real-time digital representations of physical assets and processes-offers a promising pathway to monitor, simulate, and optimize farming operations. However, engineering and societal challenges often hinder robust, field-ready DT deployment. Objectives: This study systematically identifies the key technical and socio-ethical challenges that limit DT adoption in smart agriculture and livestock. We then propose a layered, modular DT ecosystem that maps these challenges to concrete architectural mechanisms and measurable evaluation metrics. Methods: We conducted a systematic literature review of 108 WoS-indexed publications (2021-2025) and applied qualitative coding to consolidate recurring challenges into five dimensions. Guided by this evidence, we designed a multi-layer DT architecture and implemented two representative scenarios (agriculture and livestock) to quantify performance, security, privacy, and transparency metrics using simulated and open datasets. Results: The analysis indicates that scalability and interoperability have high operational impact and moderate solvability, whereas data security and ethical concerns remain major barriers in practice. In our scenarios, microservice-based design improved performance by 35%, blockchain-supported monitoring reduced breachdetection time by 65%, and explainable-AI components increased perceived transparency by 87%. We report results across latency, resource use, privacy compliance, and algorithmic fairness. Conclusion: We provide an operational, metric-driven framework that connects socio-technical DT challenges to implementable engineering solutions for agriculture and livestock. By combining a layered architecture with comparable evaluation metrics and representative scenarios, the study offers a reusable blueprint for researchers, practitioners, and policy-oriented digital-farming initiatives. This study contributes a metric-driven, multi-layer digital twin framework that systematically connects technical and societal challenges with practical implementation strategies for smart agriculture and livestock systems.
Açıklama
Anahtar Kelimeler
[Keyword Not Available]
Kaynak
Information and Software Technology
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
195
