As long as humans depend on the earth for food, innovation will always move forward. One example of this new frontier is the use of digital twin in agritech. It is a virtual copy of a real farm that uses live data to track and improve daily operations. But without accurate data, the full promise of this technology will remain out of reach. Digital twin technology notwithstanding holds the potential to transform how farms operate.
Can Digital Twin Mitigate Risk for Today’s Farms?
Digital twin in agriculture is a virtual version of a farm built from live data. It mirrors everything — soil, crops, animals, and equipment — so farmers can see what happens on the field and what could happen next. Interestingly, the concept of digital twin first appeared in 2010 when NASA used it to simulate spacecraft systems and predict potential faults before launch. Now, it’s proven to be a useful innovation in farming.
Picture a herd of livestock gathered at feed or milking stations fitted with sensors. These sensors track health, feed intake, and movement. Farmers then view this data remotely to spot early signs of illness, test new feed plans, or model barn setups that improve air flow and temperature. All this happens without stress on the animals — made possible through digital twin technology.
Crop growers also gain the same advantage. Imagine testing how a maize variety would perform in Nebraska (US) under dry weather, or how soybeans in Paraná (Brazil) might yield when planted earlier or later in the season. These digital trials help farmers plan ahead, cut waste, and adjust faster to climate shifts. Digital twin provide live and detailed models of farm systems. They let farmers and researchers test new ideas safely before making real changes.
Current Application of Digital Twin Technology in Agritech
1. John Deere (USA)
John Deere applies digital twin models to boost machine performance and predictive maintenance. The company continues to refine this process as part of its precision agriculture drive. Private 5G networks now support its factories and enable real-time data flow between machines and digital systems.
In January 2025, Deere formed new alliances with tech startups to advance digital twin and AI capabilities. The aim is to optimise land use and improve worksite safety. Jason Wallin, Senior Architect at John Deere, stated in September 2025 that the same technology stack runs across factory operations and customer equipment to create one connected ecosystem.
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2. Bayer Crop Science (Germany)
Bayer Crop Science in Germany uses digital twins to simulate crop growth and forecast yields. The company builds virtual farms that test how seeds react under different soil and weather conditions. This digital process helps refine product development and guides seed placement for stronger results. It also shortens the breeding cycle from several years to only a few months.
Through millions of virtual outcomes, Bayer now develops crop varieties that stay resilient in changing climates and ensure stable food supplies. For example, Bayer has created virtual models of its nine corn seed production facilities. Using data from the physical sites, these virtual factories allow data scientists to run rapid “what-if” simulations to optimise material flows and plan for potential changes.
Without Shared Data, Digital Twin Technology Cannot Reach Full Potential
Even with its promise, global adoption of digital twin tech remains an uphill battle. The cost of setup alone limits many small and medium farms. Aside from that, farm data comes from different sources — sensors, drones, apps, and machines. However, most systems cannot connect or share data smoothly.
This lack of integration creates gaps in how the digital twin functions. A model built from one set of data often fails to reflect the full picture of the farm. Farmers also face issues around data privacy and control, as many remain unsure who owns or benefits from the information collected on their land.
Shared data standards could change that. Groups like the International Organisation for Standardisation (ISO) and the International Data Spaces Association now draft frameworks for data quality, access, and traceability. Once these standards mature, farmers will gain tools that can connect freely across platforms, improve predictions, and support fair data use across the agricultural chain.
Bringing AI and Digital Twin Together for a More Resilient Agriculture
More can be achieved when digital twin and AI work hand in hand. Digital twin create a live picture of farm systems, while AI studies the picture to detect risks and guide decisions. In this partnership, farmers gain sharper control over outcomes, improve resource use, and act faster as field conditions shift.
The full impact, however, depends on data accuracy. Poor or incomplete information weakens both models. Shared data standards will help to unify how data is captured and exchanged across farms, sensors, and digital platforms. This link builds trust and allows digital twin to operate seamlessly across regions and systems.
Final Thoughts
The rise of digital twins signals a move toward smarter and more adaptive farming. Each season adds new lessons, while connected systems bring greater precision to how farmers plan and grow. Once data flows freely between people, machines, and models, agriculture steps closer to real resilience — where decisions come from insight and precision.
