Key Takeaways

What a Digital Twin Actually Is

The term “digital twin” gets used loosely in agricultural technology, so it is worth defining precisely what it means and what it does not mean.

A digital twin is a virtual replica of a physical system that mirrors its real-world counterpart in sufficient detail to simulate behavior, predict outcomes, and test changes without affecting the physical system. In manufacturing and aerospace—where the concept originated and is most mature—digital twins of jet engines, factory production lines, and entire supply chains have been standard practice for over a decade. These models ingest real-time sensor data from the physical system, run simulations against that data, and produce predictions that guide maintenance, optimization, and design decisions.

In indoor farming, a digital twin simulation would model the complete facility: environmental conditions (temperature, humidity, CO2, airflow), lighting (spectrum, intensity, photoperiod), irrigation and nutrient delivery, crop growth trajectories, energy consumption patterns, and operational workflows. The value proposition is simple and powerful: test before you commit. Change a lighting recipe in simulation before changing it on a production crop. Model a new HVAC configuration before spending $200,000 on equipment. Predict the energy impact of a different crop rotation before the first seed goes into the system. How AI Is Transforming Indoor Farming — From Seed to Shelf

Where Digital Twins Deliver Value Today

The applications of digital twin technology in indoor farming fall along a spectrum from near-term practical to longer-term transformative. The near-term applications are already delivering value for operators who have the data infrastructure to support them.

Pre-build facility planning is the application with the most immediate financial impact. Indoor farming facilities represent millions of dollars in capital investment, and the decisions made during design—HVAC sizing, lighting layout, growing system configuration, insulation specifications, rack spacing—lock in operating costs for the life of the facility. A digital twin of the proposed facility allows operators to model different configurations and predict their performance before committing capital. How does a 10 percent increase in insulation R-value affect annual heating costs? What happens to crop uniformity if you change the rack spacing from 24 to 20 inches? How does an additional dehumidification unit affect both humidity control and electricity consumption? These questions are expensive to answer through trial and error in a built facility. In simulation, they cost nothing.

Crop recipe optimization is the second high-value application. Every indoor farm develops growth recipes—the specific combinations of light spectrum, DLI, temperature profile, nutrient concentration, and humidity that optimize yield and quality for each crop variety. Developing those recipes through physical experimentation is slow and expensive: it requires dedicating growing space, consumables, and labor to trials that may not produce usable results. A digital twin with validated crop growth models allows operators to simulate hundreds of recipe variations in the time it takes to run one physical trial, narrowing the experimental space before committing real resources.

Energy modeling is particularly valuable in an industry where energy costs typically represent 25 to 35 percent of operating expenditure. A digital twin that models energy consumption across different scenarios—utility rate structures, time-of-use pricing, demand response participation, renewable energy integration, seasonal variation—allows operators to optimize their energy strategy with a precision that spreadsheet modeling cannot match. The difference between a well-optimized and poorly-optimized energy strategy can represent hundreds of thousands of dollars annually for a commercial-scale facility.

Operator training is an application that is often overlooked but has significant practical value. New operators learning to manage an indoor farming facility face a steep learning curve, and the consequences of mistakes during the learning period are measured in damaged crops and lost revenue. A digital twin allows new team members to practice facility management—responding to environmental alarms, adjusting crop recipes, troubleshooting equipment issues—in simulation before working with live production crops. The pilot training analogy is apt: commercial pilots spend hundreds of hours in flight simulators before flying passengers, because the cost of learning from mistakes in the real system is too high.

Predictive maintenance represents the bridge between current monitoring capabilities and full digital twin functionality. A digital twin continuously fed with real sensor data from the physical facility can detect deviations from expected equipment performance—a compressor drawing more power than its model predicts, a pump showing vibration patterns that precede failure, an LED fixture degrading faster than its specification curve. Identifying these deviations before they become failures prevents the crop losses and emergency repair costs that unplanned equipment downtime creates.

Where the Technology Stands Today

An honest assessment of digital twin technology in indoor farming requires acknowledging the gap between the vision and the current reality. Most implementations in CEA today are partial—closer to enhanced monitoring dashboards than to the full predictive simulation capability that the term “digital twin” implies in industries like aerospace or manufacturing.

The reasons are structural rather than technological. Full digital twins require three things that most indoor farms do not yet have: comprehensive sensor networks generating the volume and variety of data needed to model the complete system, standardized data formats that allow different systems (climate control, irrigation, lighting, crop monitoring) to share data seamlessly, and validated crop growth models that accurately predict biological responses to environmental inputs.

Recent research, including a September 2025 paper in npj Science of Plants, has highlighted digital twins as a key emerging technology for CEA—while emphasizing that realizing their potential requires transdisciplinary approaches combining engineering, plant science, data science, and energy management. The paper reinforces what practitioners already know: the challenge is not building better sensors or faster models. It is integrating biological complexity into computational frameworks that were designed for mechanical systems. A jet engine behaves according to well-understood physics. A plant behaves according to biology that is far less predictable and far more context-dependent.

What’s Needed to Get There

Closing the gap between current monitoring capabilities and true digital twin functionality requires progress on four fronts.

Integrated sensor networks need to move beyond environmental monitoring to include crop-level sensing—canopy temperature, leaf water potential, chlorophyll fluorescence, root zone conditions—that captures the biological state of the crop, not just the environment around it. Environmental sensors tell you what the facility is doing. Crop sensors tell you what the plants are experiencing. Both are necessary for a digital twin that models the complete system.

Standardized data formats remain a fundamental obstacle. Most indoor farms run equipment from multiple vendors, each with proprietary data formats and communication protocols. Building a digital twin from data that cannot be easily combined is like trying to assemble a puzzle from pieces made by different manufacturers—theoretically possible, but practically painful. Industry-wide data standards would accelerate digital twin development more than any single technology advancement.

Validated crop growth models are the biological core of any agricultural digital twin, and they remain the most challenging component. Plant growth responds to the complex interaction of light, temperature, humidity, CO2, nutrients, and root zone conditions in ways that are not fully captured by existing models. Validated models—tested against real production data from commercial facilities, not just research greenhouse experiments—are essential, and building them requires years of high-quality data collection.

Unified platforms that combine environmental monitoring, crop management, and economic modeling in a single system are the infrastructure layer that makes digital twins practical. The industry’s current movement toward integrated digital cultivation platforms—systems that connect sensor data, growth tracking, resource management, and financial analytics—represents a foundational step toward the data infrastructure that full digital twins require. Without that unified data layer, digital twins remain an aspiration rather than a practical tool. The Rise of Agricultural Intelligence: Why Data Is the New Soil

The Trajectory Is Clear

Digital twins in indoor farming are not a question of if but when. The technology trajectory is clear: sensor costs continue to decline, computational power continues to increase, and crop growth models continue to improve as more commercial-scale production data becomes available for validation. The aerospace and manufacturing industries have demonstrated that digital twins deliver transformative value when the data infrastructure reaches sufficient maturity. There is no fundamental reason that indoor farming cannot follow the same path.

The operators who will benefit most from digital twin technology are not necessarily the ones investing in the most sophisticated simulations today. They are the ones building the data infrastructure—integrated sensor networks, unified data platforms, consistent data collection practices—that digital twins will require. The farms that are meticulously collecting and organizing their environmental, crop, and operational data today are building the foundation for digital twin capabilities tomorrow, even if they are not calling it that.

For an industry that has spent billions learning from expensive physical experiments—building facilities that underperformed, running crop recipes that failed, sizing equipment that proved inadequate—the ability to simulate before you build, test before you implement, and predict before you invest is not a luxury. It is the technology that makes the difference between the costly trial-and-error that defined indoor farming’s first decade and the data-driven precision that will define its next one.