Electric Plant Exits Stealth With $11.2M to Decode Plant Signals With AI

Electric Plant Co. emerged from stealth on Tuesday with $11.2 million in seed funding and a handheld sensor that reads the electrical signals plants produce, then uses an AI model to translate them into data on hydration and stress.

The device, called Bud, attaches to a plant through two probes held in place by stainless steel screws. It works on any botanical with a stem at least an inch thick. On trees, the company says, it is sensitive enough to reach the cambium layer — the boundary between bark and wood.

What does the sensor actually do?

Like all living things, plants generate electricity. Electric Plant's approach is to track and decode the electrical signals plants emit in response to environmental stimuli, turning them into insights about plant conditions such as hydration and stress.

"I actually think of this as kind of a dongle," Electric Plant's cofounder and CEO, Justin Mast, told Fast Company.

The framing matters to him. The sensor is cheap and simple; the plant does the sensing.

"The plant itself is the sophisticated technology," Mast said. "Plants and trees have millions of little microreceptors that are processing information about the world around it and then they're coordinating a lot of their physical responses through electrochemical signals, and so that's what we're tapping into and listening to."

How large is the training data?

The company's AI model, named Bombadil, is trained on more than 4 million hours of electrical signals recorded from more than 1,400 plants across 100 species. The range spans tomatoes, coleus and common garden plants on one end and sequoias, apple trees, almond trees and oaks on the other.

From a plant's electrical signals, Bombadil estimates seven physiological stress markers. These include the operating efficiency of photosystem and the temperature differential between leaves and air. The model converts those markers into insights about the plant's condition.

The commercial logic is straightforward. A warming climate threatens crop yields, and fertilizer prices are rising. Electric Plant argues its technology enables more efficient planting with a higher success rate.

Who is behind it?

Mast previously founded Bloomscape, a direct-to-consumer houseplant delivery service. There, he learned firsthand how hard it is to tell customers what a plant needs at any given moment. The answer depends on sunlight exposure, geography, plant size and even proximity to a radiator.

"It's so contextual," he said. The same problem, he argues, applies at industrial scale in forestry and agriculture.

"There you have people who are way more skilled, but it's actually really hard to scale that kind of attention and that kind of understanding [of] what plants need and want," Mast said.

The speed argument is central to the pitch. Changing watering schedules or nutrients can take weeks or months to show up visually in a plant. Electric Plant promises faster knowledge, which it says unlocks operational possibilities for growers who cannot wait a season to learn a decision was wrong.

"Fundamentally, it's just about getting access to the inside language that's flowing through plants and trees so that we can better understand the experience of the world and how to better care for them," Mast said.

Who else is chasing plant language?

Electric Plant is not alone in trying to translate plant needs into plain language:

• Vivent Biosignals, a Switzerland-based plant intelligence company that also uses AI, announced in July that it surpassed 2 million monitored plant days.
• Smarty Plants, a U.K. startup making soil-based sensors for houseplants, pitched its product last month on an episode of a BBC business reality show.

The competition signals a maturing category: sensors that once measured a single variable, like soil moisture, are being replaced by systems that interpret whole-organism electrical activity. Electric Plant's $11.2 million seed round gives it the capital to prove that Bombadil's four million hours of training data translate into decisions growers will pay for.