Advancing Precision Agriculture Through Digital Twins and Smart Farming Technologies: A Review
Bibliographic record
Abstract
The agricultural sector is evolving with the adoption of smart farming technologies, where Digital Twins (DTs) offer new possibilities for real-time monitoring, simulation, and decision-making. While previous research has explored the Internet of Things (IoT), UAVs, machine learning (ML), and remote sensing (RS) in enhancing agricultural efficiency, a systematic approach to integrating these technologies within a DTs ecosystem remains underdeveloped. This paper presents a systematic review of 167 studies published between 2018 and 2025. The objective of this study is to examine recent advancements in DTs-enabled precision agriculture and propose a comprehensive framework for designing, integrating, and optimizing DTs in smart farming. The study systematically examines the current state of DT adoption, identifies key barriers, and computational efficiency challenges, and provides a step-by-step methodology for DT implementation. The review sheds light on potential future research direction and implications for policy, with the aim to speed up the adoption of DTs-based farm management systems in their operational success and commercial viability through analysis of practical applications and future perspectives. This study presents an innovative strategy for integrating digital and physical systems into agriculture and is an important contribution to existing literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".