How Extended Reality Could Transform Precision Farming

Modern farms can collect more information than ever before. Sensors monitor soil moisture, cameras identify crop stress, positioning systems guide machinery, and artificial intelligence converts raw observations into predictions. Yet much of this intelligence still reaches farmers through dashboards that are physically separated from the work being performed.

A 2026 review published in Engineering¹ argues that extended reality could close this gap by placing digital information directly within agricultural environments. Instead of checking a screen and then returning to a crop, animal, or machine, workers could see relevant measurements, warnings, and instructions while completing the task.

This positions extended reality, or XR, as more than a new category of farming hardware. It could become the interface through which farmers interact with AI, agricultural robots, digital twins, and increasingly automated equipment.

What Extended Reality Means for Agriculture

Extended reality is an umbrella term covering virtual reality, augmented reality, and mixed reality. Although these technologies are often discussed together, they serve different agricultural purposes.

Virtual reality replaces the user’s surroundings with a simulated environment. This makes it useful for machinery training, remote operation, digital twins, and testing procedures without putting equipment, workers, or crops at risk.

Augmented reality preserves the physical environment while adding digital information. A grower might look at a plant and see a disease warning, while a tractor operator could see navigation lines projected over the field. Mixed reality goes further by anchoring interactive digital objects within physical space, although the review found that agricultural MR remains less mature than AR or VR.

The important distinction is that XR does not usually generate the agricultural insight. Sensors, computer vision, and AI perform the observation and analysis. XR delivers the result in a form that can be understood and acted upon in context. This makes agricultural XR part of the broader shift toward physical AI systems that perceive conditions in the real world and connect digital intelligence with physical activity.

Closing Precision Agriculture’s Last-Mile Gap

A useful way to understand agricultural XR is as a four-stage system: perception, analysis, visualization, and guidance.

Sensors, cameras, drones, and connected machinery first collect information. AI models then analyze that information to identify disease, classify fruit, estimate yield, or detect abnormal operating conditions. XR converts the output into visible labels, directions, or alerts. The farmer completes the process by acting on that guidance.

This is a meaningful improvement over simply adding another dashboard. Agriculture is mobile, seasonal, and highly dependent on physical context. A warning that identifies the affected leaf, fruit, animal, or machine component is more actionable than an alert that requires the user to determine where the problem is located.

The paper documents several early examples. An AR-assisted grape-thinning system allowed untrained workers to achieve operational quality that exceeded experienced farmers by an average of 8.18%. Another system combining AR glasses with deep learning cut the labor required for counting rice planthoppers in half. An AR navigation system for agricultural machinery achieved positioning error below 3 centimetres and end-to-end latency under 10 milliseconds.

These results are not directly comparable because they came from different systems, tasks, and testing environments. Nevertheless, they illustrate where XR may create value: reducing the delay between recognizing a problem and responding to it.

Why Agriculture Is a Difficult Test for XR

Agricultural environments are considerably less predictable than factories or offices. Plants change shape throughout the season, animals move unpredictably, and fields expose electronics to sunlight, dust, rain, heat, and vibration. Repetitive crop rows can also confuse visual positioning systems, while leaves, branches, and moving machinery create constant occlusion.

This makes agriculture both a promising market and a severe technical test. A headset that performs well inside a warehouse may become difficult to read in direct sunlight. A computer-vision model trained on one crop may struggle with another variety, growth stage, or disease presentation. Cloud processing can provide additional computing power, but unreliable rural connectivity may introduce enough latency to make an overlay inaccurate or unsafe.

The principal barriers identified by the review include:

• Limited outdoor visibility, battery life, comfort, and durability
• High hardware, connectivity, integration, and maintenance costs
• Poor interoperability among XR, AI, IoT, and machinery platforms
• Data ownership, worker surveillance, privacy, and security concerns
• Limited long-term validation under commercial farming conditions

These limitations explain why most agricultural XR systems remain prototypes or task-specific demonstrations. The technology has shown that it can work. It has not yet consistently demonstrated that it can survive multiple seasons, generalize across farms, and produce a dependable return on investment.

The Real Opportunity Is Decision Compression

The strongest commercial case for XR is not immersion. It is decision compression.

Farm work often separates observation, diagnosis, instruction, and execution. A worker finds an abnormal plant, captures an image, consults a specialist or software platform, determines the appropriate response, and then returns to perform the task. XR can potentially compress those stages into one continuous workflow.

This also distinguishes XR from fully autonomous farming. Rather than eliminating the worker, it can increase what one person can supervise and accomplish. An operator wearing an AR display could monitor multiple agricultural robots, while a technician could receive repair instructions without removing attention from the equipment. VR could allow experienced operators to control machinery remotely or train workers before they enter a hazardous environment.

This human-machine collaboration offers an important counterpoint to the wider debate over automation. As discussed in Securities.io’s examination of how AI and robotics create different economic outcomes, the effects of automation depend partly on whether technology replaces workers or expands their capabilities. Agricultural XR is most compelling when it preserves human judgment while reducing the expertise, travel, and attention required for routine decisions.

Where Agricultural XR Could Reach the Market First

Widespread use is more likely to begin with narrow, high-value tasks than with universal headsets worn throughout the working day.

Training is one likely entry point because VR systems can simulate machinery without consuming fuel, tying up equipment, damaging crops, or exposing inexperienced operators to danger. Controlled-environment agriculture is another promising market because greenhouses offer more stable lighting, connectivity, and environmental conditions than open fields.

Maintenance, navigation, and safety-critical operations may also justify early adoption. In these cases, preventing one equipment failure, operating error, or period of downtime could offset a meaningful portion of deployment costs.

This suggests that the winners may not be companies selling standalone headsets. The more defensible position could belong to businesses controlling the underlying machinery, farm data, positioning systems, AI models, and software interfaces. A headset can be replaced. An integrated agricultural operating ecosystem is much harder to displace.

Investing in the Digitization of Farm Work

Deere & Company

For investors seeking exposure to the convergence of precision agriculture, AI, robotics, and future XR interfaces, Deere & Company offers a relevant established option.

Deere should not be considered a pure XR investment. Its relevance comes from the technological foundation required to make agricultural XR useful. The company’s precision-agriculture technology spans connected farm-management tools, guidance systems, computer vision, and increasingly autonomous machinery. These systems collect and process the field and machine data that future XR interfaces would need to display.

Those systems already collect and process the field and machine data that future AR interfaces would need to display. An operator could eventually use XR to supervise autonomous equipment, visualize planned routes, identify exceptions, or receive maintenance guidance without navigating separate control screens.

Deere also illustrates the potential competitive advantage of integration. Agricultural XR will need access to trustworthy data, machine controls, positioning technology, and service networks. Companies that already operate across those layers may be better positioned than consumer-headset manufacturers to turn XR prototypes into supported commercial tools.

The risks remain significant. Farm income and machinery demand are cyclical, advanced systems can be expensive, and customers may resist platforms that increase dependence on proprietary software. Agricultural XR itself may also take years to progress from specialized trials to material revenue. Investors should therefore view it as a possible extension of Deere’s precision-agriculture strategy, rather than a standalone investment thesis.

Extended Reality Must Prove It Belongs in the Field

Agriculture does not need virtual environments for their own sake. It needs better ways to convert rapidly expanding volumes of digital information into timely physical action.

XR offers a plausible solution by putting AI analysis, sensor readings, machine status, and instructions where decisions are made. Early studies suggest that this approach can improve task quality, reduce labor requirements, accelerate decisions, and support safer training. However, the evidence remains fragmented and heavily weighted toward prototypes.

The next stage will require rugged devices, interoperable software, reliable edge computing, clear data-governance rules, and trials that measure performance across full seasons. Most importantly, developers must prove that XR improves farm economics rather than merely making digital agriculture more visually impressive.

If those conditions are met, extended reality could become the interface connecting human judgment with the increasingly intelligent machines operating around it.

References:

¹ Liu, T., Miao, Z., Yang, S. X., Zhou, J., Gong, L., Zhang, B., Liu, C., & Zhao, C. (2026). Extended reality as a new farming tool for enhancing smart agriculture and precision farming. Engineering. https://doi.org/10.1016/j.eng.2026.08.021