Leaf Agriculture Raises $13M Series B

Leaf Agriculture announced this week a $13 million Series B co-led by Leaps by Bayer alongside a group of industry investors. The round brings Leaf's total capital raised to roughly $29 million since 2021. Existing investors also participated including S2G Ventures Trailhead Capital, SP Ventures, Spero Ventures and Cultivian.

For those new to Leaf, the company has been working to build the data infrastructure layer for agriculture. Ag data is difficult to work with and many companies in ag do not have baseline skills to structure and leverage data.

The press release compares Leaf to what Stripe is to payments or Plaid is to banking — they sit invisibly behind a lot of the digital experiences that retailers, input companies, crop insurers, and others are building on top of farm data. They process data covering more than 20% of global crop acres annually and power some of the most recognizable digital platforms in the industry, including, but not limited to, Bayer's FieldView, Syngenta's Cropwise, and BASF's xarvio.

For most of its history, the company was heavily infrastructure focused, cleaning, normalizing, and serving data so engineering and product teams at large ag companies could build on top of it. That gave them a foundation. But CEO Bailey Stockdale told me about a pattern they kept seeing where they’d work with IT and product teams, but then when they’d connect with the head sales at their partners for example, they would run into a situation where they’d seen the data, but hadn’t found a way to leverage it.

Moving from data to driving decisions is what Leaf has been working towards with them leaning much harder into dashboards, mapping tools, and consultative analytics work that gets closer to the actual business problem.

As Bailey put it to me, "We are their edge. Our goal and our job is to make sure that our customers win in their very competitive environments."

That comment resonated with me — I have frequently talked about finding information advantages in ag retail to be successful, whether it is for your sales people or operational people. Leaf helps enable that advantage by delivering insight from previously disparate data sources.

The repositioning to deliver actionable insights that deliver an edge also comes through in how Leaf has rebuilt internally. Bailey said Leaf has spent the last 12 months rebuilding the underlying systems because, in his words, "it was just too slow for agents."

Bailey’s notably avoids over indexing on AI, instead saying that he wants to ensure Leaf is always primarily focused on solving a core business problem for each customer, and the tech will follow. That said, he sees Leaf as the critical piece of bringing the value of AI to agriculture, and shared how Leaf thinks about earning the right with each customer to not only be a key piece of their AI stack, but also be their AI partner.

Two cases Bailey raised do a good job of showing what they can deliver and how they work.

The first example he shared with me was was around custom application assets at ag retail. Most retailers run sprayer and floater fleets that represent enormous capital outlays, and significant variable expenses, and are a core revenue generator for them, yet many have no rigorous way to track total cost of ownership or ROI by machine, much less by brand or operator. Leaf structures and sorts through the complex and disparate data pieces and is able to build a system that allows the retailer to understand how they are performing in aggregate, who is performing best by operator, or region, or brand of machine (eg: fuel usage). The insights can deliver benefits when doing capital plans, training teams, bonusing operators, or learning best practices internally.

The second example was surrounding biostimulants. Biostimulants are generally a placement and targeting problem. Where do these products work best, under which environmental conditions, on which farms, which enables the companies to deliver better outcomes to farmers, increase sales and become a sales person targeting tool.

Variable performance has been the long-running knock on the category and the emphasis is always to do more trials, however, there becomes a complex web to untangle to understand the specific targeting for a product. Being able to consider the exact conditions where a product wins and moving sales and marketing resources toward those areas delivers an edge to those companies. I think it brings another element surrounding the question of: what other data sources can be input in to deliver better insight? For example, could you layer in Crop Diagnostix data on top?

Ultimately, Leaf gives insight that delivers an edge, which is the only way to consistently win agribusiness.

Many companies use Leaf as a tool to collect, translate, and manage farm data from their customers. This type of use continues to be priced per acre under management, which Bailey said translates well to their underlying compute and processing costs. What's newer is a layered analytics piece. They sell that as a fixed annual fee that bundles a volume of acres (say, up to one million) with a set number of hours of technical product and engineering time for the year.

Effectively, the Leaf team goes in, sits with the customer, understands the business problem, understands the aim, and builds the dashboards and tools to support it. They are using the same “forward-deployed engineer” approach that Palantir made famous, and other AI companies are using, that is spreading across vertical software and now showing up in ag more explicitly. Bailey emphasized to me that the capital is largely earmarked for catalyzing their go-to-market initiatives including hiring the individuals who can sit with customers, identify their data problems, and work with them in solving it.

Leaf is investing in the analytics layer and the underlying data foundation because those are the things that will make AI useful when it eventually lands at a retail or input company. In the interim, Leaf is delivering insight via piecing together a complex data architectures. Most companies in agriculture are not going to be running agentic experiences in 2026, but they are asking for better understanding of what’s working, what’s not working and fopr dashboards and systems that inform igh stakes business decisions — an Leaf is delivering that today, with the capability to offer “fancier” AI functionality in the future.

Bailey continually emphasized to me their effort to lead with the business problem and earn the right to do more later. I believe for an industry that has spent two decades collecting data without always doing much with it, this is the kind of capability agribusiness want to have access to moving forward.