Publication Type
Journal Article
Journal Name
Smart Agricultural Technology
Publication Date
12-1-2026
Abstract
Sub-Saharan Africa (SSA) faces chronic food insecurity despite possessing approximately 60% of the world’s uncultivated arable land. Field trials generate evidence but are costly and insufficiently scaled to address accelerating climate and demographic pressures. Digital twin (DT) technology, defined as the continuous, bidirectional virtual replication of physical systems using real-time data, supports monitoring, modelling, and optimisation of agrifood systems. To our knowledge, however, no published synthesis has examined DT agriculture research through the lens of SSA food systems or smallholder farming realities. A PRISMA-compliant systematic review was conducted across bibliographic databases using a pre-defined Boolean search and adapted PICOS eligibility criteria. Forty peer-reviewed studies published between 2019 and 2025 were included. Structured extraction captured bibliometric, geographic, methodological, technological, and technology readiness level (TRL) data, analysed using frequency tabulation and thematic clustering. DT agriculture publications roughly tripled between 2019 and 2023 (from three to ten studies), yet 57.5% of studies remain at TRL 2–3. Europe and East Asia dominate the evidence base; no study was led by an SSA institution, and no study reported SSA-based deployment on priority crops such as maize, cassava, sorghum, or cowpea. Internet of Things (IoT) sensor infrastructure (92.5%) and artificial intelligence/machine learning (AI/ML) algorithms (52.5%) constitute the dominant technology stack. Five thematic clusters were identified: smart farming infrastructure, precision water and nutrient management, controlled environment agriculture, agrifood supply chains, and livestock and aquaculture systems. Government-funded studies tend to achieve higher TRL scores than academically funded counterparts. Synthesising these findings into a system-level, deployment-oriented analysis, we derive a conceptual SSA-DT framework. It combines offline-first edge-computing architecture, hybrid crop modelling, and multi-stakeholder interfaces, with dietary diversity and climate resilience as the two key performance indicators (KPIs), aligned with IITA’s 2024–2030 Strategy. North–South research partnerships and targeted CGIAR investment in DT technology are identified as priority actions for closing the SSA evidence gap.
Keywords
Artificial intelligence and machine learning (AI/ML), International Institute of Tropical Agriculture (IITA), Internet of Things (IoT), Precision agriculture, Technology readiness level (TRL)
Recommended Citation
Tonnang, H., Chianu, F., Mkuhlani, S., Muthoni, F., Choptiany, J., Tonle, F., Sokame, B., & Lung’aho, M. (2026). Harnessing digital twin (DT) technology for food security and climate resilience in Sub-Saharan Africa (SSA). Smart Agricultural Technology, 15 https://doi.org/10.1016/j.atech.2026.102532
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.