Unified Farm Data Layer Brings AI-Ready Agronomy Analytics to Agriculture
Agriculture has no shortage of data — tractors, satellites, soil labs, and weather systems generate more than ever. The problem, according to Bailey Stockdale, CEO and founder of Leaf Agriculture, is that none of them speaks the same language.
Tech Hub LIVE 2026: Where Ag Tech Meets Action
Discover the latest ag retail innovations, precision technology, and AI insights at Tech Hub LIVE this July in Des Moines. Learn more >>
“Farmers don’t want another dashboard,” Stockdale says. “They want fewer, richer insights from the data they already have.”
That idea sits at the center of Leaf Agriculture’s platform, built to answer agronomy questions that still take far too long to solve today — or can’t be answered at all using fragmented systems.
MORE BY LAUREN MILLIGAN
Ag Tech Talk Podcast: ICL and Lavie Bio Lead the Charge in Biological Farming Innovation
Balancing Purpose and Profit: Granite Creek’s Jim Clark on ESG, AgTech, and the Future of Agribusiness Investment
Ag Tech Talk Podcast: Tria Americas’ Jim Beneke Discusses Adaptive Artificial Intelligence in Formulation
Questions like: Which variety delivered the highest yield after controlling for soil type and fertilizer use? Or under what environmental conditions did a biological product actually perform best?
From disconnected systems to a unified data layer
Leaf connects to major agricultural data sources — including John Deere, Trimble, CNH Industrial, Ag Leader, soil labs, imagery, and weather providers — and normalizes everything into a single structure.
“We’re not the app layer and we’re not the OEM system,” Stockdale says. “We’re the layer that lets everything work together.”
That includes LeafLake, the company’s managed data environment where planted, applied, harvested, and environmental datasets are already joined and queryable using standard SQL.
For agribusiness users, that means questions that once required custom data engineering and weeks of prep can now be answered in minutes.
Wherobots: making farm data spatially usable at scale
A key piece of the stack comes from Wherobots, which handles the spatial and telemetry-heavy processing behind Leaf’s system.
According to Ben Pruden, Head of GTM at Wherobots, agriculture data becomes difficult not because it is large, but because it is spatial.
“Satellite imagery, GPS, tractor telemetry — it all needs to be processed together before it becomes useful,” Pruden says.
Wherobots processes satellite imagery, field boundaries, and machine telemetry at scale, transforming raw geospatial inputs into structured datasets that can be analyzed directly inside LeafLake.
Felt turns analysis into something agronomists can actually use
Once processed and structured, data is served into Felt, a browser-based collaborative mapping platform.
Rachel Zack, Co-founder of Felt, describes its role as the interface layer for agronomy teams.
“Agronomists don’t need heavier GIS tools,” Zack says. “They need something they can open in a browser, understand instantly, and collaborate around in real time.”
Instead of static maps or desktop GIS systems, users work inside shared, interactive field maps — layering yield, soil, application, and environmental data in one place.
What does this change mean for agribusiness teams
In practice, Leaf says the system is already being used to run cross-field and cross-season analysis that was previously too slow or fragmented to attempt.
Examples include:
- Seed variety performance analysis across soil types and weather conditions
- Biological product performance mapped against environmental variability
- Field-level comparisons across entire grower networks
“The data was always there,” Stockdale says. “It just wasn’t usable together.”
AI enters as support — not replacement
Despite the AI framing around the industry, Leaf is positioning its system firmly as decision support, not automation of agronomy judgment.
Routine tasks — anomaly detection, yield summaries, field alerts — can be automated. But interpretation and recommendation remain with agronomists and advisors.
“What’s changed isn’t who makes the decision,” Stockdale says. “It’s how quickly they can get to the right information to make it.”
Pruden puts it more bluntly: “If it needs context or experience, it stays human.”
The broader shift for ag retail and input companies
For retailers, agronomists, and input manufacturers, the implication is less about new software and more about visibility.
A unified, spatially-aware data stack means product performance, field variability, and environmental response can be analyzed across customers and regions — without rebuilding infrastructure for each question.
Stockdale frames it as infrastructure, not software: “We’re not adding another system into agriculture. We’re making sure the systems already there finally work together.”
Subscribe Today For
