Artificial intelligence startup Motorleaf closes a $2.85 million seed round to accelerate AI in greenhouse operations

Investment in artificial intelligence is heating up. In 2017 alone, venture capital investment in AI doubled to US$12 billion. With convergence of robotics, Internet of Things (IoT), we expect this trend to continue in 2018. Investors are also heavily investing in promising AI startups. Yesterday, we reported about AI startup StarMind. The company just raised $15 million to scale its self-learning AI technology.

Today, another artificial intelligence startup, Motorleaf, announced it has raised $2.85 million USD to further develop its real-time, agronomic solutions for the fast-growing controlled-environment agriculture sector. The investment, closed in two rounds with the final $2 million USD committed this May, is led by top ag- and food-tech acceleration fund Radicle Growth with support from Desjardins Capital, Real Ventures, Fluxunit (Osram Ventures), BDC Capital and 500 Startups Canada.

Founded in 2015 by Alastair Monk and Ramen Dutta, the Montreal, Canada-based startup focuses on bringing actionable, data-driven insights to greenhouse and indoor operators. MotorLeaf has been built primarily to help growers produce more, and a better quality product; by automatically adjusting to the needs of your crop. MotorLeaf builds separate components, each of which has a specific set of functions. They can operate separately, or if you add onto the MotorLeaf HEART – operate as a larger whole. With automated adjustments to your crop, and notifications on when there may be a problem for you to look into – you decrease your loss, saving / making you more money.

“Motorleaf is changing the way we grow in greenhouses and indoors with their application of enabling AI anywhere at any time,” says Radicle Growth CEO Kirk Haney. “Their technology has been proven in vegetable production environments and we are investing this capital to help the company scale.”

Motorleaf’s hardware and software hones-in on the human and environmental aspects of greenhouse production, helping to predict accurate harvest amounts in a tight-margin industry. By providing a digital agronomist, Motorleaf’s yield prediction tools help greenhouse operators meet contract obligations, better plan weekly operations and foresee production capacities in real time.

In initial trials with California greenhouse SunSelect, Motorleaf’s technology led to a 50-percent reduction in yield prediction error in tomatoes. These results were enough for SunSelect to adopt Motorleaf’s algorithms after a short trial.

“Better yield prediction is only the beginning for Motorleaf’s value to this sector,” says Motorleaf CEO Alastair Monk. “We’re ultimately producing dynamic grower protocols, which help manage everything from light and nutrients to predicting crop diseases before they happen, and optimized growing conditions that increase ROI – all based on real-time data.”

The next phase for Montreal-based Motorleaf will take a broad look at greenhouse conditions and apply the technology to multiple crops. The data insights will not be a one-time, static prediction, but allow growers to adjust growing conditions and compensate for the unexpected.

“Motorleaf’s ability to apply automation by adding convenient hardware to preexisting greenhouse control systems makes them not only practical, but ready for today’s greenhouse industry,” says Haney. “The next round of solutions coming out of testing only highlight more of the potential insights this technology brings to the table.”

Motorleaf’s AI program stands to bring applicable, cost-cutting solutions to greenhouses surrounding labor, energy, over/under production and nutrient management. There are 52.3 billion square feet of greenhouses and indoor farms that can benefit from Motorleaf’s technology today.

Haney and Lars Roessler from Fluxunit will join Motorleaf’s Board of Directors, helping the company make connections with key industry players, expanding its global reach and furthering application of the machine learning algorithms in diverse indoor agriculture facilities.