When Apollo 13 was crippled by an onboard explosion in 1970, NASA engineers on Earth had to figure out how to bring the astronauts home. They relied on physical simulators, replicas of the spacecraft’s systems, to test different solutions before instructing the crew. In 2010, NASA took this concept further by building a virtual model of a spaceship that is updated in real-time with its physical counterpart.
This was the birth of the digital twin. It was a living, evolving model that allowed engineers to predict problems before they happened. The digital twin concept, first formalised by Dr. Michael Grieves and later expanded by NASA’s John Vickers, has since spread beyond aerospace. Today, it is transforming manufacturing, healthcare, and now, agriculture.
Things got more interesting when Generative AI entered the picture. While digital twins create virtual replicas of farms, AI powers them with intelligence by analysing data, running simulations, and even generating better farming strategies. Imagine if you could test different planting techniques, irrigation schedules, or pest control methods and see the outcome before effecting it in the real world.
Source: iStock
How does Digital Twin and Generative AI Work Together in Agriculture?
If you’ve not fully grasped the concept of digital twins and how it works with generative AI in agriculture, here is an illustration to help you:
James is a wheat farmer on the outskirts of London. On his farm, James has soil sensors buried, constantly monitoring moisture, nutrient levels, and temperature. These sensors send real-time data to a cloud-based system, where a virtual replica of James’ farm is updated every second. This replica is the digital twin.
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On James’ farm, satellites and drones capture aerial images, detecting subtle crop stress before the damage is visible to the human eye. This data feeds back into the digital twin and allows James to see which areas of his farm need attention.
Now, instead of relying on experience alone, James consults his digital twin. Generative AI steps in and analyses past weather trends, current soil conditions, and satellite insights to stimulate multiple farming scenarios. It predicts how different irrigation schedules affect soil moisture over the next two weeks.
It runs tests on fertilisation strategies and identifies the best mix to maximise yield while preserving soil health. For example, James initially planned to irrigate twice a week, but the AI simulation suggests a more efficient approach. It suggests watering only once weekly because an upcoming cold front will naturally increase soil moisture.
The AI also recommends adjusting nitrogen application to prevent waste, considering past harvest data and nutrient absorption rates. By acting on these insights, James reduces water consumption, minimises fertiliser waste, and increases crop yield—all before taking a single real-world action. His farm operates more efficiently, with every decision backed by data instead of guesswork.
The Investment Opportunity
While the technology is impressive, the real opportunity lies in how agritech founders and investors can turn digital twins and AI into scalable, profitable ventures. Let’s discuss possible ventures where gen-AI and digital twins can apply:
- Digital Twins-as-a-Service (DTaaS)
Digital Twins-as-a-Service (DTaaS) is an emerging business model in which companies offer AI-powered digital twins on a subscription basis, similar to the SaaS (Software-as-a-Service) model. Instead of investing heavily in infrastructure, farmers can access on-demand AI-powered insights tailored to their farms. This allows small-scale farmers and agribusinesses to optimise operations without incurring prohibitive costs.
This is a profitable venture for investors and founders for the following reasons:
- Recurring revenue streams: The subscription model ensures a steady, predictable income stream for agritech startups.
- Scalability: The service can be expanded across multiple agricultural sectors, from crop farming to livestock management.
- Lower barriers to adoption: Farmers don’t need in-depth AI expertise—startups handle the backend, while users receive actionable insights.
- Monetising Agricultural Data
Digital twins generate an immense amount of high-value agricultural data—from soil conditions to climate trends and crop health indicators. This data holds commercial value for agribusinesses, insurance firms, supply chain companies, and policymakers who require accurate insights for risk assessments and decision-making.
How This Becomes a Business Model:
- Insurance Companies: Digital twins can provide hyper-localised weather and soil data to help insurers refine risk models and develop tailored agricultural policies.
- Supply Chain Optimisation: Food companies can use digital twin data to track crop performance and ensure better sourcing of raw materials.
- Market Intelligence: Digital twin analytics can predict crop yields, helping commodity traders and agricultural input suppliers optimise strategies.
Investment Potential:
- High-margin, data-driven business model with multiple revenue streams.
- Partnership opportunities with food security initiatives, precision agriculture firms, and government agencies.
- Reducing Risks of Agricultural Investments
Venture capitalists (VCs) and agritech investors face significant uncertainty when funding farming ventures. AI-driven digital twins can serve as risk assessment tools, simulating various farm management scenarios before investors commit capital.
Key Benefits:
- Predictive Analytics for Farm Performance: Investors can model different farming approaches and evaluate profitability before funding.
- Early Warning Systems: AI detects potential threats, such as pest outbreaks, climate risks, or soil degradation, before they escalate.
- Better ROI for Investors: By reducing uncertainty, investors gain confidence in financing scalable and sustainable agritech businesses.
Example:
A VC firm looking to invest in regenerative agriculture can use AI-powered digital twins to compare different farmland plots. The firm can then predict which locations offer the best yield and sustainability metrics before acquiring land or backing startups.
Source: iStock
- ESG & Climate Risk Modeling
Environmental, Social, and Governance (ESG) factors are becoming critical for investors focused on sustainable agriculture. AI-driven digital twins allow investors to assess climate risks and environmental impact with precision.
Why This Matters for Agritech Startups & Investors:
- Climate Resilience: Digital twins simulate the long-term effects of climate change on specific farms, allowing investors to fund climate-smart agriculture projects.
- Sustainable Resource Management: AI optimises water usage, reduces fertiliser over-application, and minimises carbon footprints, making farms more attractive for ESG-conscious investors.
- Carbon Credit Market Integration: Farms using digital twins to enhance sustainability can sell carbon credits, opening another revenue stream.
Final Note
For everyone involved in agriculture, the message is clear: the future of farming is digital. Those who embrace these technologies now will be at the forefront of a new era in agriculture. AI-powered digital twins is one of such technologies that could reshape the economic landscape of food production.
