A woman carefully plants crop plugs in a greenhouse, her hands moving with practiced precision. Above her, digital icons flicker in the air, a virtual overlay of data and analytics that mirror the physical world beneath. This blend of traditional horticulture and cutting-edge technology exemplifies the rise of digital twins in agriculture—a concept that is transforming how greenhouse growers manage their operations.
The idea of a digital twin—a virtual representation that mirrors the state, behavior, and evolution of a physical object—did not originate in farming. It emerged from product lifecycle management in the early 2000s, where engineers sought a centralized repository of product information to streamline development and maintenance. As the Internet of Things (IoT) evolved, the concept expanded to include real-time sensor data, creating a dynamic digital counterpart for physical objects. Today, digital twins are defined as virtual representations of real-world entities and processes, synchronized to reflect past, present, and even predicted future states. This technology is now making its mark on agriculture, particularly in greenhouse operations, where precision and efficiency are paramount.
For greenhouse growers, digital twins offer a powerful tool for simulation and decision support. Greenhouse horticulture relies on maintaining precise environmental conditions—temperature, humidity, light, and nutrients—all of which can be challenging to control, especially as operations scale up. Labor shortages further complicate matters, as fewer experienced growers are available to make critical decisions. Digital twins address these challenges by creating a virtual replica of the greenhouse environment. Growers can test the effects of adjusting temperature or light levels, simulate corrective treatments, and predict plant growth based on real-time data. For instance, researchers at Wageningen University & Research (WUR) have developed a digital twin that links greenhouse sensor data to 3D plant models, allowing growers to forecast light distribution, water balance, and temperature changes. This capability enables rapid responses to deviations, ensuring optimal growing conditions without the need for constant physical oversight.
Beyond simulation, digital twins enable remote monitoring and autonomous cultivation. Growers no longer need to be physically present to assess conditions or respond to issues. Instead, they can receive alerts and test interventions virtually, implementing solutions from anywhere. This shift not only improves efficiency but also supports the broader movement toward automation in agriculture, including robotics and advanced Integrated Pest Management (IPM). By reducing reliance on manual labor, digital twins help address labor shortages while improving crop health and resource management.
Energy and resource efficiency are also critical concerns in greenhouse operations, where heating, lighting, and climate control consume significant energy. Digital twins can optimize these processes by integrating IoT, artificial intelligence, and cloud computing to co-optimize production schedules, energy use, and labor costs. For example, a digital twin developed in Denmark as part of the Greenhouse Industry 4.0 project links to demand response signals from the electricity grid, allowing growers to adjust energy use when prices are favorable. Such models help reduce costs without compromising yield or quality, making operations more sustainable and economically viable.
The implications of digital twins extend beyond immediate operational benefits. They also offer valuable lessons for project management, particularly in agriculture. Digital twins provide a centralized source of truth, consolidating data from sensors, schedules, budgets, and performance metrics. This centralization supports risk management by allowing project managers to simulate interventions and evaluate their impacts before implementation. It also enhances stakeholder communication by providing visual models that illustrate the consequences of different strategies, making it easier to align growers, engineers, financiers, and regulators.
For project management education, digital twins present an opportunity to integrate technology, data, and collaboration into the curriculum. Students can study real-world cases, such as WUR’s digital tomato greenhouse, to understand how digital twins optimize resources and inform decision-making. Simulation exercises using digital twin software can help students grasp the dynamics of greenhouse operations, adjusting variables like temperature or energy prices and observing the outcomes. Cross-disciplinary collaboration is another key benefit, as digital twin projects require input from agronomists, IT specialists, data scientists, and managers. By working together, students can develop a holistic understanding of how technology and teamwork drive innovation in agriculture.
However, digital twins are not without challenges. Developing and implementing these systems requires significant expertise and investment, which may be lacking in some agricultural sectors. Connectivity issues in rural areas and limited computer literacy can also hinder adoption. Despite these hurdles, the potential of digital twins to revolutionize greenhouse operations and project management is undeniable. As the technology continues to evolve, it promises to bring greater precision, efficiency, and sustainability to agriculture, reshaping the future of farming in the process.