Revolutionizing Urban Agriculture with Digital Twin Technologies for Secure and Sustainable Cities

The integration of global challenges accelerated urbanization, climate change, food security issues, and resource scarcity has elevated urban agriculture (UA) from a marginal endeavor to an essential element of sustainable urban development. The complex and multi-faceted nature of UA frequently hinders its scalability, efficiency, and economic sustainability. This complexity involves interactions between biological systems, built environments, and socio-economic issues. Effective strategies must therefore be developed to address these challenges, fostering collaboration among policymakers, urban planners, and local communities. By leveraging innovative technologies and sustainable practices, cities can transform UA into a viable solution that enhances food security and resilience against environmental shifts. A digital Twin (DT) technology for urban agriculture is a dynamic, virtual replica of a real-world urban agricultural. This innovative model allows for real-time monitoring and analysis of various factors, such as crop health, soil conditions, and environmental impacts. By utilizing data analytics and simulation technologies, urban farmers can optimize their practices and enhance sustainability efforts. This approach improves yield and fosters a more profound understanding of ecological interactions within urban environments. As cities continue to grow, integrating digital twins in urban agriculture could play a crucial role in achieving food security and promoting greener living spaces. Moreover, this innovative strategy allows for more efficient resource management, reducing waste and minimizing the carbon footprint associated with food production. As urban agriculture evolves, these technologies will be essential in creating resilient food systems that can adapt to changing climate conditions and urban challenges. By fostering collaboration between technology and nature, urban planners can create sustainable ecosystems that benefit both residents and wildlife.

Additionally, leveraging data analytics can enhance decision-making processes, ensuring that urban agriculture thrives in harmony with the surrounding environment. This holistic approach not only addresses food security but also promotes community engagement and education about sustainable practices. As cities continue to grow, integrating these strategies will be crucial for building a healthier and more sustainable future for all urban inhabitants. Collaboration among local governments, community organizations, and residents fosters the development of innovative solutions to address challenges like climate change and resource management. These efforts will ultimately create resilient urban landscapes that prioritize both human well-being and ecological balance. Using Internet of Things (IoT) sensors, data analytics, and machine learning, a digital twin is a dynamic, virtual replica of a real thing or system that is constantly updated. In the context of urban farming, the creation of a "Farm-Twin" or "Food-System- Twin" holds the potential to bring about a revolution.

Providing high-quality, original research as well as critical evaluations that contribute to the advancement of both theoretical and practical understanding of the role that Digital Twin (DT) technology plays in the transformation of urban agricultural systems is the major objective of this special issue. This transformation requires not only investment in infrastructure but also a commitment to education and community engagement. By prioritizing these elements, urban agriculture can thrive, contributing to healthier populations and more sustainable urban ecosystems. By developing a high-fidelity digital replica of an urban farm—whether a greenhouse, vertical farm, aquaponic system, or open-air urban plot—stakeholders may simulate, forecast, and enhance the full food production lifecycle within a risk-free virtual environment.

This special issue aims to investigate the rapidly developing and still immature nexus of urban farming technology. The objective is to concentrate advanced research on how digital twin technology can tackle the fundamental issues of urban agriculture, including real-time monitoring and control of microclimates (light, temperature, humidity, and CO₂), nutrient supply, and irrigation. In addition to maximizing output and minimizing energy and water consumption, this issue will address forecasting yield, predicting plant diseases or insect outbreaks, optimizing harvest schedules, and conducting real-time environmental and economic impact studies. By establishing direct connections between urban production and distributors, merchants, and consumers within the context of a smart city, communities can be engaged in the planning and management of urban farms through the utilization of interactive digital interfaces. Through the opportunity provided by this issue, researchers in computer science, urban planning, horticulture, environmental science, and social sciences will be able to exchange findings, identify best practices, and shape the future research agenda for digital twins in urban agriculture, as it will serve as a fundamental platform. This collaborative approach not only enhances the efficiency of urban farming but also promotes a culture of ownership and responsibility. As stakeholders come together to leverage technology, they can create sustainable practices that benefit both the environment and local economies. By integrating diverse expertise, these stakeholders can also address challenges such as food security and climate resilience. Ultimately, this synergy will drive innovation and lead to more adaptive and robust urban agricultural systems. These systems can promote healthier lifestyles by providing fresh produce to urban residents while simultaneously reducing the carbon footprint associated with food transportation. As communities embrace these initiatives, they will likely inspire further engagement and investment in sustainable practices that ensure a thriving future for urban agriculture. By fostering collaboration among various sectors, cities can unlock new opportunities for economic growth and social equity. This holistic approach not only benefits the environment but also strengthens community ties and enhances overall quality of life. This focus on sustainability can lead to innovative solutions that address food insecurity and promote healthier lifestyles. As more individuals become involved, the collective impact on local ecosystems and economies will be profound, paving the way for a more resilient urban landscape. By fostering collaboration among residents, businesses, and local governments, cities can create tailored initiatives that respond to the unique needs of their communities. Such engagement not only empowers citizens but also cultivates a sense of ownership and pride in their neighborhoods, driving further progress toward a sustainable future.

The special issue invites original research articles that focus on scientific testing and robust examinations of specific hypotheses and questions concerning the way digital twin technologies could potentially be used in urban agriculture in helping cities become safer and more sustainable. Additionally, publications must utilize systematic and replicable procedures that are comprehensively delineated, serve as the foundation for the results and discussion, and are concisely summarized in the conclusion., but is not restricted to, the following areas:

• Theoretical structures and designs include innovative mathematical models and architectural examples for creating digital twins applicable to various types of urban agriculture.

• Fundamental Enabling Technologies includes IoT sensor networks and edge computing enable instantaneous data collection from urban agricultural systems. This real-time data allows farmers to make informed decisions, optimizing resource usage and improving crop yields. Additionally, these technologies facilitate predictive analytics, helping to anticipate issues before they arise.

• Utilization of big data analytics, artificial intelligence, and machine learning for model calibration, anomaly detection, and predictive simulation. These advanced technologies enable organizations to enhance their decision-making processes, optimize resource allocation, and improve overall operational efficiency. By leveraging these tools, farmers involved in urban agriculture can gain more profound insights into their data, leading to more informed strategies and innovative solutions.

• The integration of geospatial data (GIS), Building Information Modeling (BIM), and Digital Twins (DTs) is crucial for site selection and infrastructure development. This synergy enhances decision-making processes of farmers by providing real-time insights and predictive analytics of agriculture field.

Applications and Case Analyses:

• Operational decision trees for optimizing the production of specific crops in urban agriculture. These trees can help farmers identify the most efficient methods for resource allocation, pest management, and timing of planting and harvesting.

• Decision trees are used in closed-loop systems such as aquaponics and aeroponics to optimally regulate water, nutrients, and energy in urban farming. These models can effectively optimize resource use while ensuring sustainable growth conditions for plants and aquatic life in current urban agriculture.

• Decision technologies for comprehensive urban food system management, incorporating waste streams and energy networks. This approach enhances sustainability and promotes efficiency by optimizing resource use and minimizing environmental impact.

Impact of Sustainability and Resilience:

• Measuring the impact of data-driven urban analytics on urban sustainability in urban agriculture. This approach not only aids in optimizing resource usage but also fosters community engagement in sustainability initiatives in urban agriculture.

• The study aims to improve urban food resilience using stress-testing scenarios within the virtual twin environment. This approach allows for a comprehensive analysis of potential challenges and opportunities, ultimately leading to more sustainable food systems in the urban agriculture.

Socio-Economic and Human-Centric Perspectives:

• Business strategies and economic feasibility of digital technology-enabled urban agriculture. This innovative approach addresses food security and promotes sustainable practices within urban environments.

• Community involvement, education, and societal acceptance of advanced urban agriculture practices are key to promoting sustainable development.

• Cities continue to evolve, the synergy between these technologies will enable more efficient resource allocation and improved quality of life for residents.

Impediments and Prospective Opportunities:

• The influence of policy and legislation on promoting the integration of digital technologies in urban agriculture.

• Foresight on the future amalgamation of Urban Digital Twins with comprehensive smart city digital twins. This integration promises to enhance urban planning and management by providing real-time data and predictive analytics. As cities continue to evolve, the synergy between these technologies will enable more efficient resource allocation and improved quality of life for residents.

Article types and fees

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• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Digital Twin, Urban Agriculture, Precision Agriculture Vertical Farming, Smart Cities, IoT), Artificial Intelligence, Machine Learning, Sustainability Controlled Environment Agriculture (CEA)

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