Liu et al. (2025) Opportunities and challenges in the application of Digital Twins for orchard management

Identification

• Journal: Computers and Electronics in Agriculture
• Year: 2025
• Date: 2025-10-17
• Authors: Xiaojuan Liu, Leilei He, Shiao Niu, Rui Li, Yaqoob Majeed, Xuan Sun, Jinyong Chen, Xiaojuan Li, Kerry B. Walsh, Longsheng Fu
• DOI: 10.1016/j.compag.2025.111104

Research Groups

• College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
• Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China
• Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China
• Northwest A&F University Shenzhen Research Institute, Shenzhen, Guangdong 518000, China
• Department of Electrical Engineering and Computer Science, University of Wyoming, Laramie, Wyoming 82071, United States
• Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou, Henan 450009, China
• Yangling Vocational & Technical College, Yangling, Shaanxi 712100, China
• Institute of Future Farming Systems, Central Queensland University, Rockhampton, QLD 4701, Australia

Short Summary

This review article systematically summarizes the current state, opportunities, and challenges of Digital Twin (DT) applications in orchard management, aiming to optimize resource allocation and decision-making despite the inherent complexities of perennial fruit tree production. It concludes that DTs are in an exploratory stage, with a dominant focus on harvest operations, and suggests potential for standardized models and broader applications like natural disaster response.

Objective

• To systematically summarize the current state, opportunities, and challenges in the application of Digital Twins for orchard management, with the goal of optimizing resource allocation and decision-making in fruit tree production.

Study Configuration

• Spatial Scale: Orchard level, encompassing individual trees to entire orchard systems.
• Temporal Scale: Multi-seasonal to multi-year production cycles of perennial fruit trees; the review covers literature up to 2025.

Methodology and Data

• Models used: The review discusses the integration of empirical or mechanistic models within Digital Twins to predict system behavior, with examples including models for spray droplet movement within canopies.
• Data sources: Digital Twins integrate real-time sensor data and can utilize simulated data. Enabling technologies include the Internet of Things (IoT), artificial intelligence (AI), cloud computing, edge computing, extended reality, communications, and blockchain.

Main Results

• The application of Digital Twins (DTs) in orchard management is currently in an exploratory stage, constrained by the development and adoption of enabling technologies.
• DTs have been developed for various aspects of tree-fruit production, including orchard establishment, operations, harvest forecast and optimization, robotic harvesting, natural disaster response, and orchard inventory.
• The predominant focus of existing DT applications is on harvest operations, with very few applications involving a control system.
• Common patterns observed in the development of existing orchard DT models suggest the potential for standardized or universal DT models to support expanded automation operations.
• Beyond routine orchard management, DTs show significant potential for applications such as natural disaster response, offering opportunities for cost sharing and broader cross-sector benefits.

Contributions

• Provides a systematic and comprehensive review of Digital Twin applications specifically tailored to the unique complexities of perennial fruit tree production.
• Identifies the current developmental stage, key opportunities, and significant challenges in the emerging field of orchard Digital Twins.
• Highlights the potential for developing standardized DT models and expanding applications beyond routine management to areas like natural disaster response.

Funding

• No specific funding information was provided in the article text.

Citation

@article{Liu2025Opportunities,
  author = {Liu, Xiaojuan and He, Leilei and Niu, Shiao and Li, Rui and Majeed, Yaqoob and Sun, Xuan and Chen, Jinyong and Li, Xiaojuan and Walsh, Kerry B. and Fu, Longsheng},
  title = {Opportunities and challenges in the application of Digital Twins for orchard management},
  journal = {Computers and Electronics in Agriculture},
  year = {2025},
  doi = {10.1016/j.compag.2025.111104},
  url = {https://doi.org/10.1016/j.compag.2025.111104}
}