Taranis is a precision agriculture platform headquartered in Westfield, Indiana, using AI and high-resolution aerial imagery to monitor crop health and detect threats at an early stage. Its automated field analysis enables more targeted input use and supports yield optimization across large-scale farming operations.
Agriculture is navigating a difficult cycle in 2026, with market conditions shifting rapidly for growers. What pressures are farmers feeling most acutely right now?
The biggest challenge right now is general market conditions. Over the last couple of years, the rise of input costs versus generally lower commodity prices for crops like corn and soybeans has driven farmers to a place where it’s hard to generate a profit. Regulatory and environmental pressures are also adding requirements for farmers to continuously improve the way they produce.
At the same time, adopting premium technology services in this environment is difficult for both growers and ag retailers because farmers are carrying a lot of weight just to achieve profitability. Ultimately, food production is a global necessity, and by 2050, we need to double production with the same amount of arable land, so it will have to come from increasing productivity on existing land, largely driven by technology. The challenge is consistently demonstrating the value of those tools and changing behaviour — building trust, delivering consistently and proving economic value — because that kind of adoption takes time in agriculture.
While regulation can sometimes create barriers to adoption, governments are also increasingly incentivising regenerative practices. Are there any regulatory dynamics this year shaping adoption on the ground?
Overall, there is regulatory and government intent to support farming and the adoption of more regenerative practices, which aligns with what technology can unlock for farmers today.
Technology allows farmers to act more precisely and in a timely way. They can reduce inputs and costs, preserve yield and reduce environmental impact while adopting regenerative practices like integrated pest management and advanced nutrition strategies that governments want to reward. That allows farmers to improve profitability while also accessing government funding, increasing margins while improving soil health.
Taranis focuses heavily on translating field-level imagery into decisions that can be acted on quickly. Walk us through how you capture that data and turn it into actionable insight on the ground?
Providing crop intelligence requires two key pillars before you can drive action. The first is acquiring information from fields at scale — we operate in broad-acre crops across millions of acres — and imaging technology has matured to the point where it allows us to do that efficiently. We scout fields with fully automated drones: you place it on the ground, click a button, it captures the data, returns and confirms the quality. That produces a huge amount of imagery every week throughout the season.
No human can analyse that volume of data to generate value, so you need AI models that identify symptoms and generate insights that drive action. Over seven years we’ve built the largest curated agricultural data library of crop symptoms for the crops we service — across growth stages of corn, lighting conditions and weather scenarios — along with the infrastructure to ensure data quality and train the AI models on top of it.
What are some of the most common field-level issues you’re identifying that growers can intervene on?
We look for any symptom or issue in the field that can drive an action for the grower. It starts with planting issues: emergence problems, planter machinery issues and soil compaction — identifying which parts of the field may need replanting.
From there, we look for stresses a grower can mitigate or plan for — weed pressure by species, insect pressure and damage, nutrient deficiencies and diseases identified visually, not just modelled. We combine that leaf-level visibility with soil type, hybrid characteristics, weather forecasts and previous field applications, and use our AI technology and AG assistant — our generative AI language model — to support the agronomist and grower with an agronomic recommendation. Something that once took hours can now be delivered in seconds.
As you continue refining the platform and integrating more data points, where do you see the biggest remaining gap today?
The main bottleneck today is connectivity — producing more real-time insights from the field — and balancing cost versus return on investment for customers. The next inflection point is combining more data to drive optimal decisions.
That means deeper integration with input companies, ERP systems and machinery companies to build the full picture behind an agronomic decision. Today we can show growers not only what action to take, but the yield impact of acting or not acting, and whether the intervention meets an economic threshold that will ultimately be profitable. We’re also combining real-time visibility with biological models to provide more accurate in-season yield estimates, supporting not just crop input decisions but also grain marketing and broader supply management.
Can you quantify that impact in a more concrete way? How do you measure the return that growers are actually seeing?
I have a large grower customer who said: “I’ve been working my fields for generations — I know them very well. But now with your service, if you can show me the field doesn’t have weed pressure after post-emergence, I’m not going to do a post-emergence herbicide application.” Just by avoiding that application, he’s saving roughly $45 per acre — four times the cost of the entire Taranis service.
Another example is fungicide decisions. About 60% of growers don’t apply fungicides and 40% do, with about half deciding ahead of the season. It starts with knowing your yield potential. If you have a strong stand and high yield potential, it may justify protecting it with fungicide. If yield potential is already limited, it’s not worth investing in that field and you can allocate resources elsewhere. Each of these decisions creates ROI relative to the cost of Taranis.
Scaling digital agriculture often comes down to trust and behavioural change. What are you hoping the Syngenta partnership unlocks?
Scaling digital agriculture comes down to three things: consistency, trust and economic value — and you need all three. Through ag retail, Syngenta is interested in bringing services to growers that help them be more profitable. They validated that we are an AI platform that can deliver ROI and value to both the ag retailer and the grower, and want to promote this type of service across the industry.
We’re looking for partners that lead the industry. In Syngenta’s case, they bring deep agronomic knowledge and a strong crop protection portfolio that delivers real value to growers. When we align our technology with a partner like that — or Pivot Bio on the biological side — we can elevate the offering and create more value for customers.
