Abstract: Eco-unmanned farms aim to achieve precise, intelligent, and sustainable production under unmanned operations, thus integrating ecological practices, information science, and agricultural engineering. However, the heterogeneous elements included in the complex systems are the soil, crops, meteorology, hydrology, and machinery. The dynamic interactions among these components exhibit strong nonlinearity and spatiotemporal variability. However, conventional approaches can rely mainly on remote sensing and static models. Digital twin technology can be expected to construct high-fidelity virtual replicas, and then map, simulate, and interact with physical farms in real time. This review was presented on the digital twin applications in the eco-unmanned farms, covering the frameworks, key technologies, core functionalities, and closed-loop mechanisms. The challenges were also proposed for the future directions. 1) A three-layer conceptual framework comprised of the physical entity layer, the virtual model layer, and the data interaction and service layer. a) The physical entity layer was involved in intelligent agricultural machinery (e.g., unmanned tractors, plant protection UAVs, and spraying robots), an air-space-ground integrated sensing network (including satellite remote sensing, UAV remote sensing, and ground-based IoT sensors), and farmland infrastructure. This layer served as both the data source and the execution terminal for the control commands. b) In the virtual model layer, a multi-scale model library was established to integrate the mechanistic models (e.g., crop growth and soil water-salt transport models), data-driven models (e.g., convolutional neural networks and machine learning regression), and hybrid models to combine physical principles with artificial intelligence. c) The data interaction and service layer were used for the seamless data flow and closed-loop decision-making using edge computing, cloud computing, standardized interfaces, and "What-If" scenario simulation. 2) Key technologies. The framework was used to construct the digital twins in eco-unmanned farms. The air-space-ground integrated sensing network captured the multi-source data from satellites, UAVs, and ground sensors. A three-dimensional perception network was selected to cover the entire farm at multiple scales. The model library was developed to integrate the mechanistic models with physical interpretability and data-driven models, indicating the high predictive accuracy. A hybrid modeling was realized to balance the precision and computational efficiency. 3) Core functionalities. a) "What-If" ecological simulation enabled the virtual experimentation of different strategies, such as irrigation scheduling, variable-rate fertilization, and pest control. Decision support was then provided to reduce the cost and risk of field trials. b) Visualization and interaction were used to enhance user immersion and operational efficiency, including 3D reconstruction using UAV LiDAR and multi-view photography, neural radiance fields, virtual dashboards, and VR/AR interfaces. c) Optimization and collaboration were used for the global scheduling of resources, equipment, and workflows after multiple objective optimization, prediction maintenance, and energy-carbon footprint. Digital twin applications were elevated from single-point optimization to farm-wide collaboration. 4) Closed-loop application mechanisms were further examined from the bidirectional driving and data feedback. The "virtual-to-physical" driving mechanism translated the optimal decisions from the virtual space into precise execution commands, such as variable-rate prescription maps and path planning instructions for unmanned machinery. Conversely, the "physical-to-virtual" driving mechanism was used to feed the real-time monitoring data back to the virtual model, enabling dynamic state and parameter calibration. Data feedback and model optimization mechanisms were used for the digital twins with continuous self-evolution, including data assimilation, online learning, and adaptive control strategies. Several key challenges were remained on the widespread adoption of digital twins in eco-unmanned farms, including the trade-off between model accuracy and computational complexity, the technical bottleneck of multi-source heterogeneous data fusion, the dilemma of real-time performance and reliability in complex field environments, and the absence of full-lifecycle model evolution mechanisms. Future research can also be expected to focus on four directions: frameworks, key technologies, functionalities, and closed-loop mechanisms. Digital twin technology can also revolutionize the intelligent and sustainable eco-unmanned farms.