The agriculture industry is flush with data — whether it’s soil health, machinery, or field trial information. However, synthesizing and analyzing that data to realize efficiency and productivity gains requires integrating different data sources and formats, with companies like data infrastructure provider Leaf offering that expertise to the ag industry.
Founded in 2021, San Francisco-based Leaf works with some of the largest ag suppliers across the supply chain, including Bayer, BASF, Growmark, and Syngenta, connecting and analyzing their data, G. Bailey Stockdale, company founder and CEO, told AgNavigator. Leaf integrates ag companies various data sources through its API-based platform, instead of developing a digital farming app itself, he explained.
“Farm data is really valuable, but it’s also nearly impossible to work with,” Stockdale said. “The reason why it’s so hard to work with is because it’s coming in hundreds of different proprietary file formats. This is data from the machinery, data from soil labs, data from weather stations, [and] data from drones.”
He added, “In every other industry, there’s been these companies, like Plaid or Twilio in telecoms, that have basically solved the problem by connecting with all those different players and then translating that data into a single consistent format that people can consume. So, that’s what Leaf is.”
On June 10, Leaf announced it raised $13 million in a Series B round, with support from Bayer’s venture arm Leaps by Bayer and other undisclosed strategic partners. Despite continued headwinds in the agtech space, Leaf tapped into the broader AI story to attract investors, Stockdale noted.
“You really have to position yourself with what’s getting funded, and the only thing getting funded today is AI-related. We have a strong AI story at least. We didn’t over-rotate on it because I truly think that it’s going to take a few years to really start to [see these] companies really start realizing the benefits internally,” Stockdale elaborated.
Leaf rebuilds its service with AI in mind
Over the last year, Leaf rebuilt its platform to a data lake model, making it more AI-friendly and speeding up queries and analysis, Stockdale said.
For instance, if a company were to make a query about what’s the best soil type for a biological or what seed variety performed best, the previous system would have to download the data to a server and then make matches based on the request to produce the data, which was “very time-intensive,” Stockdale explained.
Leaf now brings data together in a data lake, allowing companies to query the database and gain insights quicker, he added.
“Now, what you can do is you can run one SQL query against all of them, and then you can, in the query, filter, and say ‘I want to know per soil type what the performance was,’ and the response back is an answer, and it runs in about 10 seconds,” he elaborated.
