Electric Plant Raises $11.2M to Decode Crop Signals With AI
Electric Plant has raised an $11.2 million seed round led by Onto Ventures and Climactic. The financing was reported on Oct. 20, 2026. Participation included Ponderosa, Endeavor Catalyst, Ludlow, Michigan Rise, The Fund, Schox VC and Dug Song TechCrunch.
The company was founded by Justin Mast and Lee von Kraus. Von Kraus is a neuroscientist who has worked with DARPA. The team spent three years collecting data to build Bombadil, a large language model, the AI type used for chatbots, trained to read electrical signals in a plant stem and tell growers what the plant needs.
The link is physical. Stainless steel screws fasten to the plant and pass through the cambium layer, the thin living tissue under the bark, to pick up microvolt changes, or very small shifts in voltage. Bombadil can interpret those signals in about 125 different plant species.
Electric Plant describes itself as a plant intelligence company that translates plants' electric signals into actionable insights, according to its website at electricplant.co Electric Plant. The company says its system can boost yields while reducing water use by 20% or more.
The broader context here is technical as well as farm-focused. Reading steady electrical signals as a sequence to decode means results depend on paired examples of signals and field outcomes across crops and conditions. Support for about 125 species shows breadth of input, not accuracy for each species, stability across seasons, or reliability when a grower must act on a prediction.
In my view, the hardware choice deserves as much attention as the model. A screw through the cambium gives direct contact with tiny voltage changes, at the cost of piercing the plant and limiting how many units can be installed and maintained. That tradeoff will shape daily use and cost more than model design alone. Growers will weigh installation effort, sensor longevity and data integration against existing irrigation practice.
Looking at near-term use, the test is narrow. Can stem voltage flag a specific action early enough and reliably enough to change a decision a grower would otherwise make differently. Yield gains and 20% lower water use are system outcomes that depend on sensing plus models plus grower action plus field variability. If Electric Plant can document that chain in commercial fields, it opens a path to closed-loop control, where plant state, not schedule or soil reading, drives watering. That possibility is worth watching.
